{
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"title": "GPLSI: Word Coarse-grained Disambiguation aided by Basic Level Concepts *",
"authors": [
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"first": "Rub\u00e9n",
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"last": "Izquierdo",
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"laboratory": "GPLSI Group",
"institution": "DLSI University of Alicante",
"location": {
"country": "Spain"
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"first": "Armando",
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"last": "Su\u00e1rez",
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"laboratory": "GPLSI Group",
"institution": "DLSI University of Alicante",
"location": {
"country": "Spain"
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"abstract": "We present a corpus-based supervised learning system for coarse-grained sense disambiguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level Concepts automatically obtained from WordNet. Base Level Concepts are some synsets that generalize a hyponymy sub-hierarchy, and provides an extra level of abstraction as well as relevant information about the context of a word to be disambiguated. Our experiments proved that using this type of features results on a significant improvement of precision. Our system has achieved almost 0.8 F1 (fifth place) in the coarse-grained English all-words task using a very simple set of features plus Base Level Concepts annotation.",
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"text": "We present a corpus-based supervised learning system for coarse-grained sense disambiguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level Concepts automatically obtained from WordNet. Base Level Concepts are some synsets that generalize a hyponymy sub-hierarchy, and provides an extra level of abstraction as well as relevant information about the context of a word to be disambiguated. Our experiments proved that using this type of features results on a significant improvement of precision. Our system has achieved almost 0.8 F1 (fifth place) in the coarse-grained English all-words task using a very simple set of features plus Base Level Concepts annotation.",
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"text": "The GPLSI system in SemEval's task 7, coarsegrained English all-words, consists of a corpusbased supervised-learning method which uses local context information. The system uses Base Level Concepts (BLC) (Rosch, 1977) as features. In short, BLC are synsets of WordNet (WN) (Fellbaum, 1998) that are representative of a certain hyponymy sub-hierarchy. The synsets that are selected to be BLC must accomplish certain conditions that will be explained in next section. BLC are slightly different from Base Concepts of Eu-roWordNet 1 (EWN) , Balkanet 2 or Meaning Project 3 because of the selection criteria but also because our method is capable to define them automatically. This type of features helps our system to achieve 0.79550 F1 (over the First-Sense baseline, 0.78889) while only four systems outperformed ours being the F1 of the best one 0.83208.",
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"section": "Introduction",
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"text": "WordNet has been widely criticised for being a sense repository that often offers too fine-grained sense distinctions for higher level applications like Machine Translation or Question & Answering. In fact, WSD at this level of granularity, has resisted all attempts of inferring robust broad-coverage models. It seems that many word-sense distinctions are too subtle to be captured by automatic systems with the current small volumes of word-sense annotated examples. Possibly, building class-based classifiers would allow to avoid the data sparseness problem of the word-based approach.",
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"text": "Thus, some research has been focused on deriving different sense groupings to overcome the finegrained distinctions of WN (Hearst and Sch\u00fctze, 1993) (Mihalcea and Moldovan, 2001 ) (Agirre et al., 2003) and on using predefined sets of sense-groupings for learning class-based classifiers for WSD (Segond et al., 1997) (Ciaramita and Johnson, 2003) (Villarejo et al., 2005) (Curran, 2005) (Ciaramita and Altun, 2006) . However, most of the later approaches used the original Lexicographical Files of WN (more recently called Super-senses) as very coarse-grained sense distinctions. However, not so much attention has been paid on learning class-based classifiers from other available sense-groupings such as WordNet Domains (Magnini and Cavaglia, 2000) , SUMO labels (Niles and Pease, 2001 ), EuroWordNet Base Concepts or Top Concept Ontology labels (Atserias et al., 2004) . Obviously, these resources relate senses at some level of abstraction using different semantic criteria and properties that could be of interest for WSD. Possibly, their combination could improve the overall results since they offer different semantic perspectives of the data. Furthermore, to our knowledge, to date no comparative evaluation have been performed exploring different sense-groupings.",
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"text": "This paper is organized as follows. In section 2, we present a method for deriving fully automatically a number of Base Level Concepts from any WN version. Section 3 shows the details of the whole system and finally, in section 4 some concluding remarks are provided.",
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"text": "The notion of Base Concepts (hereinafter BC) was introduced in EWN. The BC are supposed to be the concepts that play the most important role in the various wordnets 4 (Fellbaum, 1998) of different languages. This role was measured in terms of two main criteria:",
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"text": "\u2022 A high position in the semantic hierarchy;",
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"text": "\u2022 Having many relations to other concepts;",
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"text": "Thus, the BC are the fundamental building blocks for establishing the relations in a wordnet and give information about the dominant lexicalization patterns in languages. BC are generalizations of features or semantic components and thus apply to a maximum number of concepts. Thus, the Lexicografic Files (or Supersenses) of WN could be considered the most basic set of BC.",
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"text": "Basic Level Concepts (Rosch, 1977) should not be confused with Base Concepts. BLC are the result of a compromise between two conflicting principles of characterization:",
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"text": "http://wordnet.princeton.edu #rel. synset 18 group 1,grouping 1 19 social group 1 37 organisation 2,organization 1 10 establishment 2,institution 1 12 faith 3,religion 2 5 Christianity 2,church 1,Christian church 1 #rel. synset 14 entity 1,something 1 29 object 1,physical object 1 39 artifact 1,artefact 1 63 construction 3,structure 1 79 building 1,edifice 1 11 place of worship 1, ... 19 church 2,church building 1 #rel. synset 20 act 2,human action 1,human activity 1 69 activity 1 5 ceremony 3 11 religious ceremony 1,religious ritual 1 7 service 3,religious service 1,divine service 1 1 church 3,church service 1 \u2022 Represent as many concepts as possible;",
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"text": "\u2022 Represent as many features as possible;",
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"text": "As a result of this, Basic Level Concepts typically occur in the middle of hierarchies and less than the maximum number of relations. BC mostly involve the first principle of the Basic Level Concepts only.",
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"text": "Our work focuses on devising simple methods for selecting automatically an accurate set of Basic Level Concepts from WN. In particular, our method selects the appropriate BLC of a particular synset considering the relative number of relations encoded in WN of their hypernyms.",
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"text": "The process follows a bottom-up approach using the chain of hypernym relations. For each synset in WN, the process selects as its Base Level Concept the first local maximum according to the relative number of relations. For synsets having multiple hypernyms, the path having the local maximum with higher number of relations is selected. Usually, this process finishes having a number of \"fake\" Base Level Concepts. That is, synsets having no descendants (or with a very small number) but being the first local maximum according to the number of relations considered. Thus, the process finishes checking if the number of concepts subsumed by the preliminary list of BLC is higher than a certain threshold. For those BLC not representing enough concepts according to a certain threshold, the process selects the next local maximum following the hypernym hierarchy. An example is provided in table 1. This table shows the possible BLC for the noun \"church\" using WN1.6. The table presents the hypernym chain for each synset together with the number of relations encoded in WN for the synset. The local maxima along the hypernym chain of each synset appears in bold. Table 2 presents the polysemy degree for nouns and verbs of the different words when grouping its senses with respect the different semantic classes on SensEval-3. Senses stand for the WN senses, BLC for the Automatic BLC derived using a threshold of 20 and SuperSenses for the Lexicographic Files of WN.",
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"text": "The GPLSI system uses a publicly available implementation of Support Vector Machines, SVMLight 5 (Joachims, 2002) , and Semcor as learning corpus. Semcor has been properly mapped and labelled with both BLC 6 and sense-clusters.",
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"text": "Actually, the process of training-classification has two phases: first, one classifier is trained for each possible BLC class and then the SemEval test data is classified and enriched with them, and second, a classifier for each target word is built using as additional features the BLC tags in Semcor and SemEval's test.",
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"text": "Then, the features used for training the classifiers are: lemmas, word forms, PoS tags 7 , BLC tags, and first sense class of target word (S1TW). All features 5 http://svmlight.joachims.org/ 6 Because BLC are automatically defined from WN, some tuning must be performed due to the nature of the task 7. We have not enough room to present the complete study but threshold 20 has been chosen, using SENSEVAL-3 English all-words as test data. Moreover, our tests showed roughly 5% of improvement against not using these features.",
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"text": "7 TreeTagger (Schmid, 1994) was used were extracted from a window [\u22123.. + 3] except for the last type (S1TW). The reason of using S1TW features is to assure the learning of the baseline. It is well known that Semcor presents a higher frequency on first senses (and it is also the baseline of the task finally provided by the organizers). Besides, these are the same features for both first and second phases (obviously except for S1TW because of the different target set of classes). Nevertheless, the training in both cases are quite different: the first phase is class-based while the second is word-based. By word-based we mean that the learning is performed using just the examples in Semcor that contains the target word. We obtain one classifier per polysemous word are in the SemEval test corpus. The output of these classifiers is a sensecluster. In class-based learning all the examples in Semcor are used, tagging those ones belonging to a specific class (BLC in our case) as positive examples while the rest are tagged as negatives. We obtain so many binary classifiers as BLC are in Se-mEval test corpus. The output of these classifiers is true or f alse, \"the example belongs to a class\" or not. When dealing with a concrete target word, only those BLC classifiers that are related to it are \"activated\" (i.e, \"animal\" classifier will be not used to classify \"church\"), ensuring that the word will be tagged with coherent labels. In order to avoid statistical bias because of very large set of negative examples, the features are defined from positive examples only (although they are obviously used to characterize all the examples).",
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"start": 13,
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"text": "The WSD task seems to have reached its maximum accuracy figures with the usual framework. Some of its limitations could come from the sensegranularity of WN. In particular, SemEval's coarsegrained English all-words task represents a solution in this direction.",
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"section": "Conclusions and further work",
"sec_num": "4"
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"text": "Nevertheless, the task still remains oriented to words rather than classes. Then, other problems arise like data sparseness just because the lack of adequate and enough examples. Changing the set of classes could be a solution to enrich training corpora with many more examples Another option seems to be incorporating more semantic information.",
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"section": "Conclusions and further work",
"sec_num": "4"
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"text": "Base Level Concepts (BLC) are concepts that are representative for a set of other concepts. A simple method for automatically selecting BLC from WN based on the hypernym hierarchy and the number of stored relationships between synsets have been used to define features for training a supervised system. Although in our system BLC play a simple role aiding to the disambiguation just as additional features, the good results achieved with such simple features confirm us that an appropriate set of BLC will be a better semantic discriminator than senses or even sense-clusters.",
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"section": "Conclusions and further work",
"sec_num": "4"
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"text": "http://www.illc.uva.nl/EuroWordNet/ http://www.ceid.upatras.gr/Balkanet http://www.lsi.upc.es/ nlp/meaning",
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