| { | |
| "paper_id": "R11-1032", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T15:05:06.618478Z" | |
| }, | |
| "title": "Improving WSD using ISR-WN with Relevant Semantic Trees and SemCor Senses Frequency", | |
| "authors": [ | |
| { | |
| "first": "Yoan", | |
| "middle": [], | |
| "last": "Guti\u00e9rrez", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Matanzas", | |
| "location": { | |
| "country": "Cuba" | |
| } | |
| }, | |
| "email": "yoan.gutierrez@umcc.cu" | |
| }, | |
| { | |
| "first": "Sonia", | |
| "middle": [], | |
| "last": "V\u00e1zquez", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Alicante", | |
| "location": { | |
| "country": "Spain" | |
| } | |
| }, | |
| "email": "svazquez@dlsi.ua.es" | |
| }, | |
| { | |
| "first": "Andr\u00e9s", | |
| "middle": [], | |
| "last": "Montoyo", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Alicante", | |
| "location": { | |
| "country": "Spain" | |
| } | |
| }, | |
| "email": "montoyo@dlsi.ua.es" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "In this paper we concentrate on the resolution of the semantic ambiguity that arises when a given word has several meanings. This specific task is commonly referred to as Word Sense Disambiguation (WSD). We propose a method that obtains the appropriate senses from a multidimensional analysis (using Relevant Semantic Trees). Our method uses different resources WordNet, WordNet Domains, WordNet-Affects and SUMO, combined with senses frequency obtained from SemCor. Our hypothesis is that in WSD it is important to obtain the most frequent senses depending on the type of analyzed context to achieve better results. Finally, in order to evaluate and compare our results, it is presented a comprehensive study and experimental work using the Senseval-2 and Semeval-2 data set, demonstrating that our system obtains better results than other unsupervised systems.", | |
| "pdf_parse": { | |
| "paper_id": "R11-1032", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "In this paper we concentrate on the resolution of the semantic ambiguity that arises when a given word has several meanings. This specific task is commonly referred to as Word Sense Disambiguation (WSD). We propose a method that obtains the appropriate senses from a multidimensional analysis (using Relevant Semantic Trees). Our method uses different resources WordNet, WordNet Domains, WordNet-Affects and SUMO, combined with senses frequency obtained from SemCor. Our hypothesis is that in WSD it is important to obtain the most frequent senses depending on the type of analyzed context to achieve better results. Finally, in order to evaluate and compare our results, it is presented a comprehensive study and experimental work using the Senseval-2 and Semeval-2 data set, demonstrating that our system obtains better results than other unsupervised systems.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "The main goal of knowledge technologies is to provide meaning to the huge quantity of information that our multilingual societies generate day to day. A wide range of advanced techniques are required to progressively automate the knowledge lifecycle. For that, after performing an analysis to large data collections it is necessary to develop different approaches to automatically represent and manage a high-level of meaningful concepts (Montoyo et al., 2005) . Moreover, to be able to create efficient systems of Natural Language Processing (NLP) it is necessary to turn the information extracted from words in plain text into a Concept Level or meaningful word senses. This representation allows to group words with similar meanings according to the context where they appear.", | |
| "cite_spans": [ | |
| { | |
| "start": 438, | |
| "end": 460, | |
| "text": "(Montoyo et al., 2005)", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "In order to determine the right meanings of words in different contexts WSD systems have been developed. Furthermore, it has been proved that applications such as Machine Translation, Information Extraction, Question Answering, Information Retrieval, Text Classification, and Text Summarization require knowledge about word meanings to obtain better results. So, WSD is considered an essential task for all these applications (Ide and V\u00e9ronis, 1998) . For this reason many research groups are working on WSD, using a wide range of approaches.", | |
| "cite_spans": [ | |
| { | |
| "start": 426, | |
| "end": 449, | |
| "text": "(Ide and V\u00e9ronis, 1998)", | |
| "ref_id": "BIBREF16" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Due to the need of evaluating different approaches to show the improvements of NLP tasks it was created the Senseval 1 competition. The first Senseval was in 1998 at Herstmonceux Castle, Succex (England) and after that every three years a new competition takes place. In Senseval, different NLP tasks are defined in order to evaluate systems using the same repositories and corpus. At present, the results obtained in WSD have been going poorer, because the requirements of each corpus are getting more complex. For example, in Senseval-2 (Cotton et al., 2001) the best system obtained a 69% of accuracy in WSD, three years later in Senseval-3 (Snyder and Palmer, 2004) the best results were around 65.2% of accuracy, next in Semeval-1 (Pradhan et al., 2007) a 59.1% of accuracy was obtained and in Semeval-2 (Agirre et al., 2010) was 55.5%.", | |
| "cite_spans": [ | |
| { | |
| "start": 539, | |
| "end": 560, | |
| "text": "(Cotton et al., 2001)", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 644, | |
| "end": 669, | |
| "text": "(Snyder and Palmer, 2004)", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 726, | |
| "end": 758, | |
| "text": "Semeval-1 (Pradhan et al., 2007)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 809, | |
| "end": 830, | |
| "text": "(Agirre et al., 2010)", | |
| "ref_id": "BIBREF10" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Due to the fact that the results are still very low in accuracy we want to go deeply in the resolution of semantic ambiguity. Firstly, we have studied the behavior of the baseline Most Frequent Sense (MFS) in each competition. This baseline has been placed among the top places of the rank; for example, in Senseval-2 a system applying this baseline could have been located on the 2 nd place with a 64.58% of accuracy (Preiss, 2006) . In Senseval-3 Denys Yuret of Koc University computed a 60.9% and for the same competition Bart Decadt of University of Antwerp provided a baseline of 62.4%, these results could have been located on 7 th and 5 th positions respectively (Snyder and Palmer, 2004) . In Semeval-1 the baseline was positioned on 9 th place of fourteen systems and for the Semeval-2 competition the MFS baseline was located on 6 th place. As we can see, this probabilistic procedure can obtain effective results on WSD task, but notice that it does not take into account context information.", | |
| "cite_spans": [ | |
| { | |
| "start": 418, | |
| "end": 432, | |
| "text": "(Preiss, 2006)", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 670, | |
| "end": 695, | |
| "text": "(Snyder and Palmer, 2004)", | |
| "ref_id": "BIBREF5" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Taking into account these facts our hypothesis is that for WSD it is important to obtain the most frequent senses combined with contextual information.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "After these considerations, a new question arises: How will we be able to develop a procedure that uses the sense frequencies combined with a technique that takes into account the context information and improves the MFS results?", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "With the aim to answer this question and to demonstrate our hypothesis we present the following contributions:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "\uf0b7 A method that combines MFS with a multidimensional analysis of the context. It uses several semantic resources combined with Relevant Semantic Trees. \uf0b7 An analysis of how the MFS influences on the Relevant Semantic Trees method. \uf0b7 An analysis of the behavior of Relevant Semantic Trees and Most Frequent Senses in each one of the semantic dimensions. \uf0b7 A voting process between MFS and the results of different semantic dimensions. \uf0b7 An exhaustive evaluation of the proposal. \uf0b7 A comparison between our results and the systems in the Senseval-2 and Semeval-2 competitions. In Section 2 we show some related works. Our approach is described in Section 3. The evaluations and analysis are provided in Section 4. Finally, we conclude in Section 5 adding further works. Among all of these resources, ISR-WN has the highest quantity of semantic dimensions aligned, so it is a suitable resource to run our proposal. Next, we present a brief description of ISR-WN.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Integration of Semantic Resources based on WordNet (ISR-WN) (Guti\u00e9rrez et al., 2010b ) is a new resource that allows the integration of several semantic resources mapped to WN. In ISR-WN, WordNet is used as a core to link several resources such as: SUMO (Niles, 2001) , WordNet Domains (WND) (Magnini and Cavaglia, 2000) and WordNet Affect (WNA) (Strapparava and Valitutti, 2004) . As (Guti\u00e9rrez et al., 2010a) describe, the integrator resource provides a software capable to navigate inside the semantic network. In order to apply the multidimensionality that this resource provides, we have analyzed related NLP approaches that take into account semantic dimensionality. Addressed to context analysis we have studied (Magnini et al., 2008) , (V\u00e1zquez et al., 2004) and (Buscaldi et al., 2005) . In these works WSD is performed using the WND resource (domain dimension). (Zouaq et al., 2009) , (Villarejo et al., 2005) among others, conducted a semantic analysis using SUMO ontology (category dimension), and the Relevant Semantic Trees (RST) (Guti\u00e9rrez et al., 2010a) apply several dimensions at once.", | |
| "cite_spans": [ | |
| { | |
| "start": 60, | |
| "end": 84, | |
| "text": "(Guti\u00e9rrez et al., 2010b", | |
| "ref_id": "BIBREF22" | |
| }, | |
| { | |
| "start": 254, | |
| "end": 267, | |
| "text": "(Niles, 2001)", | |
| "ref_id": "BIBREF12" | |
| }, | |
| { | |
| "start": 292, | |
| "end": 320, | |
| "text": "(Magnini and Cavaglia, 2000)", | |
| "ref_id": "BIBREF3" | |
| }, | |
| { | |
| "start": 346, | |
| "end": 379, | |
| "text": "(Strapparava and Valitutti, 2004)", | |
| "ref_id": "BIBREF7" | |
| }, | |
| { | |
| "start": 385, | |
| "end": 410, | |
| "text": "(Guti\u00e9rrez et al., 2010a)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 719, | |
| "end": 741, | |
| "text": "(Magnini et al., 2008)", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 744, | |
| "end": 766, | |
| "text": "(V\u00e1zquez et al., 2004)", | |
| "ref_id": "BIBREF20" | |
| }, | |
| { | |
| "start": 771, | |
| "end": 794, | |
| "text": "(Buscaldi et al., 2005)", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 872, | |
| "end": 892, | |
| "text": "(Zouaq et al., 2009)", | |
| "ref_id": "BIBREF2" | |
| }, | |
| { | |
| "start": 895, | |
| "end": 919, | |
| "text": "(Villarejo et al., 2005)", | |
| "ref_id": "BIBREF15" | |
| }, | |
| { | |
| "start": 1044, | |
| "end": 1069, | |
| "text": "(Guti\u00e9rrez et al., 2010a)", | |
| "ref_id": "BIBREF21" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Integration of Semantic Resources based on WordNet (ISR-WN)", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "Next, we present the RST method which is able to work with different resources based on WordNet.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Integration of Semantic Resources based on WordNet (ISR-WN)", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "The RST method is able to find the correct senses of each word using Relevant Semantic Trees from different resources. This approach can be used with many resources mapped to WN as we have mentioned above.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Relevant Semantic Trees (RST)", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "In order to measure the association between concepts according to a multidimensional perspective in each sentence, RST uses an Association Ratio (AR) modification based on the proposal presented by V\u00e1zquez et al. (2004) .", | |
| "cite_spans": [ | |
| { | |
| "start": 198, | |
| "end": 219, | |
| "text": "V\u00e1zquez et al. (2004)", | |
| "ref_id": "BIBREF20" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Relevant Semantic Trees (RST)", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "We propose an unsupervised knowledge-based method that uses the original RST technique including senses frequency of SemCor 4 corpus and using a voting process to find the right senses. The voting process involves MFS (Most Frequent Sense), RST over WND, WNA, WN taxonomy and SUMO. Adding this new information we are able to improve the previous results obtained by the original RST and we also improve the MFS results in Semeval-2 competition. Specifically, we provide a sort of supervised aid (i.e. MFS) to the RST method of Guti\u00e9rrez et al.(2010a) Step 3. Voting process to obtain the final senses. Next, we present how these phases have been developed.", | |
| "cite_spans": [ | |
| { | |
| "start": 527, | |
| "end": 550, | |
| "text": "Guti\u00e9rrez et al.(2010a)", | |
| "ref_id": "BIBREF21" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "WSD Method", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "In this section, we describe how we have used a fragment of the original RST method with the aim to obtain Relevant Semantic Trees from the sentences. Equation 1 is used to measure and obtain the values of Relevant Concepts:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "(1) Where 2Where C is a concept; s is a sentence or a set of words (w); s i is the i-th word (w) of the sentence s; P(C, w) is joint probability distribution; and P(C) is marginal probability.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "The first stage is to Pre-process the sentence to obtain all lemmas. For instance, in the sentence \"But it is unfair to dump on teachers as distinct from the educational establishment.\" the lemmas are: [unfair, dump, teacher, distinct, educational, establishment Next, each lemma is searched through ISR-WN resource and it is correlated with concepts of WND (the dimension used in this example). Table 1 shows the results after applying Equation 1 over the sentence.", | |
| "cite_spans": [ | |
| { | |
| "start": 202, | |
| "end": 262, | |
| "text": "[unfair, dump, teacher, distinct, educational, establishment", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 396, | |
| "end": 403, | |
| "text": "Table 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "After obtaining the Initial Concept Vector of Domains we apply the Equation 3 in order to build the Relevant Semantic Tree related to the sentence.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "; 3Where:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "; 4Where AR(PC, s) represents the AR value of PC related to the sentence s;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "is the AR value calculated with Equation 1 in case of Child Concept (ChC) was included in the Initial Vector, otherwise is calculated with the Equation 3; ND is a Normalized Distance; IC is the Initial Concept from we have to add the ancestors; PC is Parent Concept; TD is Depth of the hierarchic tree of the resource to use; and MP is Minimal Path.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Applying the Equation 3, the algorithm to decide which parent concept will be added to the vector is shown here: Figure 1 shows. As we can see, the Relevant Semantic Tree of domains in Figure 1 has associated a color intensity related to the AR value of each domain. The more intense the color is the more related AR is. (Magnini and Cavaglia, 2000) . Moreover, after conducting several experiments we have confirmed that it introduced errors.", | |
| "cite_spans": [ | |
| { | |
| "start": 321, | |
| "end": 349, | |
| "text": "(Magnini and Cavaglia, 2000)", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 113, | |
| "end": 121, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 185, | |
| "end": 193, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "if (", | |
| "eq_num": "> 0" | |
| } | |
| ], | |
| "section": "Obtaining the Relevant Semantic Trees", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "To select the correct senses, three steps are applied:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Selecting the correct senses", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Step 1. Obtaining the RST from candidate senses In this step we associate to each possible sense of each lemma a RST based on each semantic dimension. At this stage the aim of RST is to measure the relation between each Concept and each sense. To do this we use the Equation 2 where we have substituted the variable w (word) with the variable sw i , (sense) where sw i indicates the i-th sense of word w. As a result, we convert each RST in a vector. Next, we continue with the complete process adding the parent concepts.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Selecting the correct senses", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "To measure the similarity between the RST of the sentences and senses, we have applied a fragment of the original method from (Guti\u00e9rrez et al., 2010a) introducing sense frequency ( ) as a new modification. Our goal is to obtain a new value to measure the Most Frequent Sense (MFS) in a given context. The AR value is accumulated when a matching exists between the vector elements of the sense and the vector elements of the sentence. The process is shown in the Equation 5.", | |
| "cite_spans": [ | |
| { | |
| "start": 126, | |
| "end": 151, | |
| "text": "(Guti\u00e9rrez et al., 2010a)", | |
| "ref_id": "BIBREF21" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Step 2. Obtaining accumulated values of relevance for each resource and frequency sense", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "(", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": ";", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Where AC is the AR value accumulated for the analyzed elements; ARV is the vector of relevant concepts of the sentence with the format: AC is calculated for each RST (or Relevant Vector) of each semantic dimension. In this approach we have obtained four AC values (for WN taxonomy, WND, WNA and SUMO).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": ";", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "ARV[concept1 | AR", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": ";", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Notice that once we have obtained AC values for each sense in each dimension, if the senses calculated do not match with the grammatical category that Freeling (Atserias et al., 2006) suggests, we discriminate these senses adding a zero value to AC; in other case we add a one value. Adding these values we can maintain all the candidates in the solution despite the grammatical category is wrong.", | |
| "cite_spans": [ | |
| { | |
| "start": 160, | |
| "end": 183, | |
| "text": "(Atserias et al., 2006)", | |
| "ref_id": "BIBREF13" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": ";", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Finally, the proposed sense will have the highest AC value among all senses in each lemma.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": ";", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Step", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": ";", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "As we have explained above, each semantic dimension provides a possible sense. It is important to remark that the sense frequency is also included as a semantic dimension. So, in order to decide the right sense among the different semantic dimensions sense proposals we use a voting process. To apply this idea we define the next equation:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Voting process to obtain the final senses", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "; (6)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Voting process to obtain the final senses", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "Where VAC corresponds to a vector composed by AC values of each sense for one lemma; V [VAC] is a vector of the VAC; k corresponds to each resource; : corresponds to k-th VAC for resource k;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Voting process to obtain the final senses", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": ": determines the sense with maximum AC value of each VAC; i: is i-th sense;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Voting process to obtain the final senses", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": ": determines the sense that was selected more times by max k among all resources; and Ps: indicates proposed sense.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Voting process to obtain the final senses", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "The obtain a tie or disjoin senses, the proposed sense will be the most frequent. We have chosen this option because of empirical studies have demonstrated that MFS works better than others (Molina et al., 2002) .", | |
| "cite_spans": [ | |
| { | |
| "start": 190, | |
| "end": 211, | |
| "text": "(Molina et al., 2002)", | |
| "ref_id": "BIBREF0" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Voting process to obtain the final senses", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "In this section our purpose is to confirm the hypothesis presented in Section 1. We have evaluated this method with two different test corpus, Senseval-2 on \"English All words\" task and Semeval-2 on \"English All words on Specific Domain\" task. Moreover, we have compared our results with the participating systems of the aforementioned competitions. The goal of these experiments is to demonstrate how the sense frequencies combined with RST can improve the original RST results.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluations and Analysis", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "First, we analyzed how the addition of the sense frequencies to accumulated value (AC) of each sense improved the results of the previous work published on (Guti\u00e9rrez et al., 2010a) . To do this we used as test corpus the file d00.txt and we conducted some experiments: \uf0b7 Exp 1: Adding to AC value a 0% of Freq s . \uf0b7 Exp 2: Adding to AC value a 50% of Freq s . \uf0b7 Exp 3: Adding to AC value a 100% of Freq s . In the original method the authors calculated an accumulated value for each resource and summed up all the values to obtain the total accumulated value to combine all resources. In this new approach we also add the Freq s to the total accumulated value. Table 3 shows how each experiment obtains better results when Sense Frequencies (Freq s ) parameter is increased. Notice that we do not keep increasing this weight (i.e. 150%, 200%, etc) because the proposal would become converted only in selection process of MFS.", | |
| "cite_spans": [ | |
| { | |
| "start": 156, | |
| "end": 181, | |
| "text": "(Guti\u00e9rrez et al., 2010a)", | |
| "ref_id": "BIBREF21" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 662, | |
| "end": 669, | |
| "text": "Table 3", | |
| "ref_id": "TABREF7" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In order to determine whether the Freq s enhances the Most Frequent Senses (MFS) baseline, we conducted new experiments.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "Next, we show how we have used the original method adding to AC the 100% of Freq s but only using one dimension at the same time:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "\uf0b7 Exp4: Using MFS using Freq s \uf0b7 Exp5: Using WND resource \uf0b7 Exp6: Using SUMO resource \uf0b7 Exp7: Using WNA resource \uf0b7 Exp8: Using WN Taxonomy resource", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "After doing these experiments we were able to determine which dimension worked better. As we can see on Table 3 , these five experiments obtained promising results.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 104, | |
| "end": 111, | |
| "text": "Table 3", | |
| "ref_id": "TABREF7" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "Another experiment was to combine these five experiments in a voting process to obtain even better results. This idea has led us to make our main proposal.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "\uf0b7 Exp9: Applying a voting process among Exp4, Exp5, Exp6, Exp7 and Exp8 results Table 3 shows all the results obtained from d00.txt file of Senseval-2. The result of MFS is underlined and the approach that exceeded it is in bold. We can see that the voting process (Exp9) obtained the best results. Following, we present the results after analyzing the entire corpus of the Senseval-2 competition. For that, we applied two experiments to the entire corpus.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 80, | |
| "end": 87, | |
| "text": "Table 3", | |
| "ref_id": "TABREF7" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "\uf0b7 Exp10: Applying WSD with MFS of Freq s \uf0b7 Exp11: Applying a voting process using the five dimensions We show in Table 4 a comparison among the results of the best performances of our voting process, MFS using Freq s and MFS obtained by (Preiss, 2006) . The baseline used by Preiss was based on cntlist file from WN 1.7 version and our Exp10 was based on cntlist from WN 1.6. Notice, that are different although both are based on frequency information. As we can see, our approach improves the Exp10 results. These results were obtained by our system, but the baseline MFS results obtained by Preiss were better than ours. This means that we could enhance the MFS that we use. So, we need to integrate in our approach a better MFS resource to obtain better results. Table 4 shows that our proposal would have the best results of all unsupervised methods.", | |
| "cite_spans": [ | |
| { | |
| "start": 237, | |
| "end": 251, | |
| "text": "(Preiss, 2006)", | |
| "ref_id": "BIBREF14" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 113, | |
| "end": 120, | |
| "text": "Table 4", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 766, | |
| "end": 773, | |
| "text": "Table 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Evaluation with Senseval-2 corpus", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "Our approach was also evaluated using corpus from Semeval-2 competition. The voting process obtained 52.7% and 51.5% of precision and recall respectively, improving the MFS baseline with 1% of accuracy. The original method from (Guti\u00e9rrez et al., 2010a) was improved on 19.3% of accuracy such as Table 5 shows. The underlined results pertain to original method from (Guti\u00e9rrez et al., 2010a) and the bold results pertain to our approach. As a result, we can see that we can improve the MFS proposal from Semeval-2 competition.", | |
| "cite_spans": [ | |
| { | |
| "start": 228, | |
| "end": 253, | |
| "text": "(Guti\u00e9rrez et al., 2010a)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 366, | |
| "end": 391, | |
| "text": "(Guti\u00e9rrez et al., 2010a)", | |
| "ref_id": "BIBREF21" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 296, | |
| "end": 303, | |
| "text": "Table 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Evaluation with Semeval-2 corpus", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "In this competition only were evaluated nouns and verbs. The behavior of our approach for each category was: nouns 54.4% of precision and 53.7% of recall, and verbs 49.4% of precision and 45.4% of recall. Each category is effective in comparison with the best results obtained on this competition.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation with Semeval-2 corpus", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "In order to determine if the annotation of grammatical categories influences on the results, we discovered that the Freeling tool introduced a noise of 2.62% when detecting nouns and for verbs 8.20%. These analyses indicate that the results would be better using another more accurate tool.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation with Semeval-2 corpus", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "In this section we present a comparison with some relevant WSD methods. We can mention those approaches using page-rank such as (Sinha and Mihalcea, 2007) , and (Agirre and Soroa, 2009 ). These proposals were tested using \"English All Words\" task corpus from Senseval-2. In both proposals, Page-Rank method has been used to determine the centrality of structural lexical network using the semantic relations of WordNet. Then, to disambiguate each word the most weighted sense was chosen. These approaches obtained 58.6% and 56.37% of recall respectively. Other significant work is the ACL 2004 paper by (Mc.Carthy et al., 2004) where the most frequent senses were obtained from a variety of resources (Reuters Corpus and SemCor Corpus), some of which provide domain information. This proposal obtained a 64% of precision in all-nouns task; this is just 3% higher than our results. However, we achieved better results than Mihalcea and Agirre exceeding them around 5%. This improvement could seem very poor but talking about WSD is a great step forward.", | |
| "cite_spans": [ | |
| { | |
| "start": 128, | |
| "end": 154, | |
| "text": "(Sinha and Mihalcea, 2007)", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 161, | |
| "end": 184, | |
| "text": "(Agirre and Soroa, 2009", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 603, | |
| "end": 627, | |
| "text": "(Mc.Carthy et al., 2004)", | |
| "ref_id": "BIBREF9" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Comparison with newer works", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "In this paper we have presented the hypothesis that for word-sense disambiguation it is important to obtain the Most Frequent Senses depending on the kind of analyzed context. In order to demonstrate this hypothesis, we have studied how several semantic dimensions combined with sense frequencies could improve the obtained results of many approaches that only conducted the WSD analysis with one dimension. We have proposed an adaptation of an unsupervised knowledge-based method that combines the original Relevant Semantic Trees method with senses frequency in a voting process. As a result, we have been able to determine which percentage of sense frequency is needed to help the Relevant Semantic Trees method. Therefore, we have demonstrated that the WSD results are better when more percentage of sense frequency is added.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions and further works", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Moreover, we have conducted different experiments in order to know which semantic dimensions achieve better results. These experiments demonstrated that the Domain dimension (WND) and WordNet dimension (WN Taxonomy) worked better than MFS (Frequency dimension). Also, a voting process has been applied among all dimensions obtaining in Senseval-2 an of 60.9% and achieving the best results of all unsupervised systems. Furthermore, related to Semeval-2 our approach has improved the baseline MFS and the original RST method.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions and further works", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "As further work we propose to use other resources on the voting process in order to add more dimensions and also, use a better frequency resource. Apart from that, we also have considered to use another grammatical categorizer, in order to reduce the noise introduced by misclassifying words.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions and further works", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Valenciana (grant no. PROMETEO/2009/119, ACOMP/2010/288 and ACOMP/2011/001).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions and further works", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "http://www.senseval.org", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "http://multiwordnet.fbk.eu/ 3 http://www.lsi.upc.es/~nlp/meaning/meaning.html", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "http://www.cse.unt.edu/~rada/downloads.html#semcor", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "This paper has been supported partially by Ministerio de Ciencia e Innovaci\u00f3n -Spanish Government (grant no. TIN2009-13391-C04-01), and Conselleria d'Educaci\u00f3n -Generalitat", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgements", | |
| "sec_num": null | |
| } | |
| ], | |
| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Word Sense Disambiguation using Statistical Models and WordNet", | |
| "authors": [ | |
| { | |
| "first": "Antonio", | |
| "middle": [], | |
| "last": "Molina", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ferran", | |
| "middle": [], | |
| "last": "Pla", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Encarna", | |
| "middle": [], | |
| "last": "Segarra", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Lidia", | |
| "middle": [], | |
| "last": "Moreno", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2002, | |
| "venue": "Proceedings of 3rd International Conference on Language Resources and Evaluation", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Antonio Molina, Ferran Pla, Encarna Segarra and Lidia Moreno. 2002. Word Sense Disambiguation using Statistical Models and WordNet. Proceedings of 3rd International Conference on Language Resources and Evaluation: Las Palmas de Gran Canaria.", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Combining Knowledge-and Corpus-based Word-Sense-Disambiguation Methods", | |
| "authors": [ | |
| { | |
| "first": "Andr\u00e9s", | |
| "middle": [], | |
| "last": "Montoyo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Armando", | |
| "middle": [], | |
| "last": "Su\u00e1rez", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "German", | |
| "middle": [], | |
| "last": "Rigau", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Manuel", | |
| "middle": [], | |
| "last": "Palomar", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "Journal of Artificial Intelligence", | |
| "volume": "23", | |
| "issue": "", | |
| "pages": "299--330", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Andr\u00e9s Montoyo, Armando Su\u00e1rez, German Rigau and Manuel Palomar. 2005. Combining Knowledge-and Corpus-based Word-Sense- Disambiguation Methods. Journal of Artificial Intelligence, 23:299-330.", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "A SUMO-based Semantic Analysis for Knowledge Extraction", | |
| "authors": [ | |
| { | |
| "first": "Amal", | |
| "middle": [], | |
| "last": "Zouaq", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Michel", | |
| "middle": [], | |
| "last": "Gagnon", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Benoit", | |
| "middle": [], | |
| "last": "Ozell", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2009, | |
| "venue": "Proceedings of the 4th Language & Technology Conference", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Amal Zouaq, Michel Gagnon and Benoit Ozell. 2009. A SUMO-based Semantic Analysis for Knowledge Extraction. Proceedings of the 4th Language & Technology Conference: Pozna\u0144, Poland.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "Integrating Subject Field Codes into WordNet", | |
| "authors": [ | |
| { | |
| "first": "Bernardo", | |
| "middle": [], | |
| "last": "Magnini", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Gabriela", | |
| "middle": [], | |
| "last": "Cavaglia", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2000, | |
| "venue": "Proceedings of Third International Conference on Language Resources and Evaluation (LREC-2000", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "1413--1418", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Bernardo Magnini and Gabriela Cavaglia. 2000. Integrating Subject Field Codes into WordNet. Proceedings of Third International Conference on Language Resources and Evaluation (LREC-2000): 1413--1418.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "Using Domain Information for Word Sense Disambiguation", | |
| "authors": [ | |
| { | |
| "first": "Bernardo", | |
| "middle": [], | |
| "last": "Magnini", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Carlo", | |
| "middle": [], | |
| "last": "Strapparava", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Giovanni", | |
| "middle": [], | |
| "last": "Pezzulo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Alfio", | |
| "middle": [], | |
| "last": "Gliozzo", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2008, | |
| "venue": "Proceedings of the First International Conference on Emerging Trends in Engineering and Technology", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "1187--1191", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Bernardo Magnini, Carlo Strapparava, Giovanni Pezzulo and Alfio Gliozzo. 2008. Using Domain Information for Word Sense Disambiguation. Proceedings of the First International Conference on Emerging Trends in Engineering and Technology (icetet 2008): 1187-1191. Nagpur, India.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "SENSEVAL-3: Third International Workshop on the evaluation of System of the Semantic Analysis of Text", | |
| "authors": [ | |
| { | |
| "first": "Benjamin", | |
| "middle": [], | |
| "last": "Snyder", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Martha", | |
| "middle": [], | |
| "last": "Palmer", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Benjamin Snyder and Martha Palmer. 2004. The English All Word Task. SENSEVAL-3: Third International Workshop on the evaluation of System of the Semantic Analysis of Text: Barcelona, Spain.", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "WordNet. An Electronic Lexical Database", | |
| "authors": [ | |
| { | |
| "first": "Christiane", | |
| "middle": [], | |
| "last": "Fellbaum", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1998, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Christiane Fellbaum. 1998. WordNet. An Electronic Lexical Database. The MIT Press: University of Cambridge.", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "WordNet-Affect: an affective extension of WordNet", | |
| "authors": [ | |
| { | |
| "first": "Carlo", | |
| "middle": [], | |
| "last": "Strapparava", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Alessandro", | |
| "middle": [], | |
| "last": "Valitutti", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "1083--1086", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Carlo Strapparava and Alessandro Valitutti. 2004. WordNet-Affect: an affective extension of WordNet. Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004): 1083-1086. Lisbon.", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "Integrating Conceptual Density with WordNet Domains and CALD Glosses for Noun Sense Disambiguation", | |
| "authors": [ | |
| { | |
| "first": "Davide", | |
| "middle": [], | |
| "last": "Buscaldi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Paolo", | |
| "middle": [], | |
| "last": "Rosso", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Francesco", | |
| "middle": [], | |
| "last": "Masulli", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "Proceedings of Espa\u00f1a for Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "267--276", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Davide Buscaldi, Paolo Rosso and Francesco Masulli. 2005. Integrating Conceptual Density with WordNet Domains and CALD Glosses for Noun Sense Disambiguation. Proceedings of Espa\u00f1a for Natural Language Processing (ESTAL-2005): 267- 276. Alicante, Spain.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "Finding Predominant Word Senses in Untagged Text", | |
| "authors": [ | |
| { | |
| "first": "Diana", | |
| "middle": [], | |
| "last": "Mc", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rob", | |
| "middle": [], | |
| "last": "Carthy", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Julie", | |
| "middle": [], | |
| "last": "Koeling", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Weeds", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Carroll", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "279--286", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Diana Mc.Carthy, Rob Koeling, Julie Weeds and John Carroll. 2004. Finding Predominant Word Senses in Untagged Text. Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04): 279--286.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "SemEval-2010 task 17: All-words word sense disambiguation on a specific domain", | |
| "authors": [ | |
| { | |
| "first": "Eneko", | |
| "middle": [], | |
| "last": "Agirre", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christiane", | |
| "middle": [], | |
| "last": "Oier L\u00f3pez De Lacalle", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Shu-Kai", | |
| "middle": [], | |
| "last": "Fellbaum", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Maurizio", | |
| "middle": [], | |
| "last": "Hsieh", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Monica", | |
| "middle": [], | |
| "last": "Tesconi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Monachini", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "Proceedings of the 5th International Workshop on Semantic Evaluation", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "75--80", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Eneko Agirre, Oier L\u00f3pez De Lacalle, Christiane Fellbaum, Shu-Kai Hsieh, Maurizio Tesconi, Monica Monachini, Piek Vossen and Roxanne Segers. 2010. SemEval-2010 task 17: All-words word sense disambiguation on a specific domain. Proceedings of the 5th International Workshop on Semantic Evaluation: 75-80. Los Angeles, California.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Personalizing PageRank for Word Sense Disambiguation", | |
| "authors": [ | |
| { | |
| "first": "Eneko", | |
| "middle": [], | |
| "last": "Agirre", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Aitor", | |
| "middle": [], | |
| "last": "Soroa", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2009, | |
| "venue": "Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Eneko Agirre and Aitor Soroa. 2009. Personalizing PageRank for Word Sense Disambiguation. Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics (EACL-2009): Athens, Greece.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "Mapping WordNet to the SUMO Ontology", | |
| "authors": [ | |
| { | |
| "first": "Ian", | |
| "middle": [], | |
| "last": "Niles", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2001, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ian Niles. 2001. Mapping WordNet to the SUMO Ontology. Teknowledge Corporation.", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "FreeLing 1.3: Syntactic and semantic services in an opensource NLP library", | |
| "authors": [ | |
| { | |
| "first": "Jordi", | |
| "middle": [], | |
| "last": "Atserias", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Bernardino", | |
| "middle": [], | |
| "last": "Casas", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Elisabet", | |
| "middle": [], | |
| "last": "Comelles", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Meritxell", | |
| "middle": [], | |
| "last": "Gonz\u00e1lez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2006, | |
| "venue": "Proceedings of LREC'06", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Jordi Atserias, Bernardino Casas, Elisabet Comelles, Meritxell Gonz\u00e1lez, Lluis Padr\u00f3 and Muntsa Padr\u00f3. 2006. FreeLing 1.3: Syntactic and semantic services in an opensource NLP library. Proceedings of LREC'06: Genoa, Italy.", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "A detailed comparison of WSD systems:an analysis of the system answers for the Senseval-2 English all words task", | |
| "authors": [ | |
| { | |
| "first": "Judita", | |
| "middle": [], | |
| "last": "Preiss", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2006, | |
| "venue": "Natural Language Engineering", | |
| "volume": "12", | |
| "issue": "3", | |
| "pages": "209--228", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Judita Preiss. 2006. A detailed comparison of WSD systems:an analysis of the system answers for the Senseval-2 English all words task. Natural Language Engineering, 12(3):209-228.", | |
| "links": null | |
| }, | |
| "BIBREF15": { | |
| "ref_id": "b15", | |
| "title": "Exploring the construction of semantic class classiers for WSD. Sociedad Espa\u00f1ola para el Procesamiento del Lenguaje Natural", | |
| "authors": [ | |
| { | |
| "first": "Luis", | |
| "middle": [], | |
| "last": "Villarejo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Llu\u00eds", | |
| "middle": [], | |
| "last": "M\u00e1rquez", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "German", | |
| "middle": [], | |
| "last": "Rigau", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "", | |
| "volume": "35", | |
| "issue": "", | |
| "pages": "195--202", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Luis Villarejo, Llu\u00eds M\u00e1rquez and German Rigau. 2005. Exploring the construction of semantic class classiers for WSD. Sociedad Espa\u00f1ola para el Procesamiento del Lenguaje Natural, 35: 195-202.", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art", | |
| "authors": [ | |
| { | |
| "first": "Nancy", | |
| "middle": [], | |
| "last": "Ide", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jean", | |
| "middle": [], | |
| "last": "V\u00e9ronis", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1998, | |
| "venue": "Computational Linguistics", | |
| "volume": "24", | |
| "issue": "1", | |
| "pages": "1--40", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Nancy Ide and Jean V\u00e9ronis. 1998. Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art. Computational Linguistics, 24(1):1-40.", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity", | |
| "authors": [ | |
| { | |
| "first": "Ravi", | |
| "middle": [], | |
| "last": "Sinha", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rada", | |
| "middle": [], | |
| "last": "Mihalcea", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2007, | |
| "venue": "Proceedings of the IEEE International Conference on Semantic Computing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ravi Sinha and Rada Mihalcea. 2007. Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity. Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2007): Irvine, CA.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "English Lexical Sample SRL and All Words. Proceedings of the 4th International Workshop on Semantic Evaluations", | |
| "authors": [ | |
| { | |
| "first": "S", | |
| "middle": [], | |
| "last": "Sameer", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Edward", | |
| "middle": [], | |
| "last": "Pradhan", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Dmitriy", | |
| "middle": [], | |
| "last": "Loper", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Martha", | |
| "middle": [ | |
| "Stone" | |
| ], | |
| "last": "Dligach", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Palmer", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2007, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "87--92", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Sameer S. Pradhan, Edward Loper, Dmitriy Dligach and Martha Stone Palmer. 2007. SemEval-2007 Task-17: English Lexical Sample SRL and All Words. Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval- 2007): 87-92. Prague.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "English All word. SENSEVAL-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems", | |
| "authors": [ | |
| { | |
| "first": "Scott", | |
| "middle": [], | |
| "last": "Cotton", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Phil", | |
| "middle": [], | |
| "last": "Edmonds", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Adam", | |
| "middle": [], | |
| "last": "Kilgarriff", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Martha", | |
| "middle": [], | |
| "last": "Palmer", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2001, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Scott Cotton, Phil Edmonds, Adam Kilgarriff and Martha Palmer. 2001. English All word. SENSEVAL-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems: Toulouse, France.", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "Using Relevant Domains Resource for Word Sense Disambiguation. IC-AI'04", | |
| "authors": [ | |
| { | |
| "first": "Sonia", | |
| "middle": [], | |
| "last": "V\u00e1zquez", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Andr\u00e9s", | |
| "middle": [], | |
| "last": "Montoyo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "German", | |
| "middle": [], | |
| "last": "Rigau", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Proceedings of the International Conference on Artificial Intelligence", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Sonia V\u00e1zquez, Andr\u00e9s Montoyo and German Rigau. 2004. Using Relevant Domains Resource for Word Sense Disambiguation. IC-AI'04. Proceedings of the International Conference on Artificial Intelligence: Ed: CSREA Press. Las Vegas, E.E.U.U.", | |
| "links": null | |
| }, | |
| "BIBREF21": { | |
| "ref_id": "b21", | |
| "title": "UMCC-DLSI: Integrative resource for disambiguation task", | |
| "authors": [ | |
| { | |
| "first": "Yoan", | |
| "middle": [], | |
| "last": "Guti\u00e9rrez", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Antonio", | |
| "middle": [], | |
| "last": "Fern\u00e1ndez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "Proceedings of the 5th International Workshop on Semantic Evaluation", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "427--432", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yoan Guti\u00e9rrez, Antonio Fern\u00e1ndez, Andr\u00e9s Montoyo and Sonia V\u00e1zquez. 2010a. UMCC-DLSI: Integrative resource for disambiguation task. Proceedings of the 5th International Workshop on Semantic Evaluation: 427-432. Uppsala, Sweden.", | |
| "links": null | |
| }, | |
| "BIBREF22": { | |
| "ref_id": "b22", | |
| "title": "Integration of semantic resources based on WordNet. XXVI Congreso de la Sociedad Espa\u00f1ola para el Procesamiento del Lenguaje Natural", | |
| "authors": [ | |
| { | |
| "first": "Yoan", | |
| "middle": [], | |
| "last": "Guti\u00e9rrez", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Antonio", | |
| "middle": [], | |
| "last": "Fern\u00e1ndez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "", | |
| "volume": "45", | |
| "issue": "", | |
| "pages": "161--168", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yoan Guti\u00e9rrez, Antonio Fern\u00e1ndez, Andr\u00e9s Montoyo and Sonia V\u00e1zquez. 2010b. Integration of semantic resources based on WordNet. XXVI Congreso de la Sociedad Espa\u00f1ola para el Procesamiento del Lenguaje Natural, 45: 161-168. Universidad Polit\u00e9cnica de Valencia, Valencia.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "TABREF2": { | |
| "text": "]", | |
| "content": "<table><tr><td/><td>Vector</td></tr><tr><td>AR Domain</td><td>AR Domain</td></tr><tr><td>0.90 Pedagogy</td><td>0.36 Commerce</td></tr><tr><td colspan=\"2\">0.90 Administration 0.36 Quality</td></tr><tr><td>0.36 Buildings</td><td>0.36 Psychoanalysis</td></tr><tr><td>0.36 Politics</td><td>0.36 Economy</td></tr><tr><td>0.36 Environment</td><td/></tr><tr><td colspan=\"2\">Table 1. Initial Vector of Domain</td></tr></table>", | |
| "type_str": "table", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF3": { | |
| "text": "This bottom-up process is applied for each Concept of the Initial Vector to add each Relevant Parent to the vector. After reproducing the process to each Concept of the Initial Vector, the Relevant Semantic Tree is built. As a result, theTable 2is obtained. This vector represents the Domain tree associated to the sentence such as", | |
| "content": "<table><tr><td>){</td></tr><tr><td>if ( PC had not been added to vector)</td></tr><tr><td>PC is added to the vector with AR(PC, s)</td></tr><tr><td>value;</td></tr><tr><td>else PC value = PC value + AR(PC, s) value; }</td></tr></table>", | |
| "type_str": "table", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF4": { | |
| "text": "Final Domain Vector based on WND Figure 1. Relevant Semantic Tree from WNDOnce the Relevant Semantic Tree is obtained, in case of the Domain dimension the Factotum category is eliminated from the tree. Due to the fact that Factotum is a generic Domain associated to words that appear in general contexts it does not provide useful information", | |
| "content": "<table><tr><td colspan=\"2\">Vector</td></tr><tr><td>AR Domain</td><td>AR Domain</td></tr><tr><td colspan=\"2\">1.63 Social_Science 0.36 Buildings</td></tr><tr><td colspan=\"2\">0.90 Administration 0.36 Commerce</td></tr><tr><td>0.90 Pedagogy</td><td>0.36 Environment</td></tr><tr><td>0.80 RootDomain</td><td>0.11 Factotum</td></tr><tr><td colspan=\"2\">0.36 Psychoanalysis 0.11 Psychology</td></tr><tr><td>0.36 Economy</td><td>0.11 Architecture</td></tr><tr><td>0.36 Quality</td><td>0.11 Pure_Science</td></tr><tr><td>0.36 Politics</td><td/></tr></table>", | |
| "type_str": "table", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF5": { | |
| "text": "Vs[concepts]; Vs k is the k-th concept of the vector Vs; ARV [Vs k ] represents the value of AR assigned to the concept Vs k for the value ARV; Freq s represents the normalized value of frequency sense obtained from cntlist file from WN 1.6; and is the term that normalizes the result.", | |
| "content": "<table/>", | |
| "type_str": "table", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF7": { | |
| "text": "Results over d00.txt from Senseval-2", | |
| "content": "<table/>", | |
| "type_str": "table", | |
| "num": null, | |
| "html": null | |
| } | |
| } | |
| } | |
| } |