De Cock, Martine

Hoste, Veronique

Lefever, Els

English

Ghent University Academic Bibliography

Using Parallel Corpora for Word SenseDisambiguationEls Lefever a,b Ve´ronique Hoste a,b,c Martine De Cock ba LT3, Language and Translation Technology Team, University College Ghentb Dept. of Applied Mathematics and Computer Science, Ghent Universityc Dept. of Linguistics, Ghent University* The full paper version of this paper is published in the Proceedings of the 49th Annual Meeting of theAssociation for Computational Linguistics, Portland, Oregon, June 19-24, 2011.1 IntroductionWord Sense Disambiguation (WSD) is the Natural Language Processing (NLP) task that consists in selectingthe correct sense of a polysemous word in a given context. Most state-of-the-art WSD systems are supervisedclassifiers that are trained on manually sense-tagged corpora, which are very time-consuming and expensiveto build. In order to overcome this acquisition bottleneck (sense-tagged corpora are scarce for languagesother than English), we decided to take a multilingual approach to WSD, that builds up the sense inventoryon the basis of the Europarl parallel corpus [3]. Using translations from a parallel corpus implicitly dealswith the granularity problem as finer sense distinctions are only relevant as far as they are lexicalized in thetarget translations. It also facilitates the integration of WSD in multilingual applications such as multilingualInformation Retrieval (IR) or Machine Translation (MT).2 Experimental SetupStarting point of the experiments was the six-lingual sentence-aligned Europarl corpus that was used inthe SemEval-2010 “Cross-Lingual Word Sense Disambiguation” (CLWSD) task [4]. The task is a lexicalsample task for twenty English ambiguous nouns that consists in assigning a correct translation in the fivesupported target languages (viz. French, Italian, Spanish, German and Dutch) for an ambiguous focus wordin a given context. In order to detect the relevant translations for each of the twenty ambiguous focuswords, we ran GIZA++ [5] with its default settings for all focus words. This word alignment output wasthen considered to be the label for the training instances for the corresponding classifier (e.g. the Dutchtranslation is the label that is used to train the Dutch classifier). By considering this word alignment outputas oracle information, we redefined the CLWSD task as a classification task. To train our five classifiers(English as input language and French, German, Dutch, Italian and Spanish as focus languages), we used thememory-based learning (MBL) algorithm implemented in TIMBL [1], which has successfully been deployedin previous WSD classification tasks [2].For our feature vector creation, we combined a set of English local context features and a set of binarybag-of-words features that were extracted from the aligned translations. First all English sentences werepreprocessed by means of a memory-based shallow parser (MBSP) [1] that performs tokenization, Part-of-Speech tagging and text chunking. The preprocessed sentences were used as input to build a set of commonlyused WSD features related to the English input sentence: (a) features related to the focus word itself beingthe word form of the focus word, the lemma, Part-of-Speech and chunk information and (b) local contextfeatures related to a window of three words preceding and following the focus word containing for each ofthese words their full form, lemma, Part-of-Speech and chunk information.In addition to these well known monolingual features, we extracted a set of binary bag-of-words featuresfrom the aligned translation that are not the target language of the classifier (e.g. for the Dutch classifier, weextracted bag-of-words features from the Italian, Spanish, French and German aligned translations). Inorder to extract useful content words, we first ran Part-of-Speech tagging and lemmatisation by means of theTreetagger [6] tool. Per ambiguous focus word, a list of content words (nouns, adjectives, verbs and adverbs)was extracted that occurred in the aligned translations of the English sentences containing the focus word.One binary feature per selected content word was then created per ambiguous word: ‘0’ in case the worddoes not occur in the aligned translation of this instance, and ‘1’ in case the word does occur in the alignedtranslation of the training instance. For the creation of the bag-of-words features for the test sentenceshowever, we needed to adopt a different approach as we do not have aligned translations for the Englishtest instances at our disposal. We decided to deploy a novel strategy that uses the Google Translate API1 toautomatically generate a translation for all English test instances in the five supported languages. In a nextstep the automatically generated translation was preprocessed in the same way as the training translations(Part-of-Speech-tagged and lemmatized). The resulting lemmas were then used to construct the same set ofbinary bag-of-words features that were stored for the training instances of the ambiguous focus word. Forour experiments we trained three flavors of the ParaSense system that incorporate different feature sets: (1)both the local context and translation features, (2) translation bag-of-words features and (3) English localcontext features.3 EvaluationAs evaluation metrics, we used both the BEST precision metric from the SemEval “Cross-Lingual WordSense Disambiguation” task as well as a straightforward accuracy measure. As a baseline, we selected themost frequent lemmatized translation that resulted from the automated word alignment (GIZA++). We alsocompared our results with the two winning SemEval-2 systems for the Cross-Lingual Word Sense Disam-biguation task. The classification results show that all three flavors of the ParaSense system easily beat thebaseline. Although we applied a very basic strategy for the selection of our bag-of-words translation features(we did not perform any filtering on the translations except for Part-of-Speech information), we observe thatfor three languages the full feature vector outperforms the classifier that uses the more traditional WSD localcontext features. For Dutch, the classifier that merely uses translation features even outperforms the classi-fier that uses the local context features. Moreover, the ParaSense system clearly outperforms the winningSemEval systems, except for Spanish where the scores are similar. As all systems, viz. the two SemEvalsystems as well as the three flavors of the ParaSense system, were trained on the same Europarl data, thescores illustrate the potential advantages of using a multilingual approach.References[1] W. Daelemans and A. van den Bosch. Memory-based Language Processing. Cambridge UniversityPress, 2005.[2] V. Hoste, I. Hendrickx, W. Daelemans, and A. van den Bosch. Parameter Optimization for Machine-Learning of Word Sense Disambiguation. Natural Language Engineering, Special Issue on Word SenseDisambiguation Systems, 8:311–325, 2002.[3] P. Koehn. Europarl: a parallel corpus for statistical machine translation. In Tenth Machine TranslationSummit, pages 79–86, Phuket, Thailand, 2005.[4] E. Lefever and V. Hoste. SemEval-2010 Task 3: Cross-Lingual Word Sense Disambiguation. In Pro-ceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 15–20, Uppsala,Sweden, 2010.[5] F. J. Och and H. Ney. A systematic comparison of various statistical alignment models. ComputationalLinguistics, 29(1):19–51, 2003.[6] H. Schmid. Probabilistic part-of-speech tagging using decision trees. In Proceedings of the InternationalConference on new methods in Language Processing, Manchester, UK, 1994.1http://code.google.com/apis/language/

Using parallel corpora for word sense disambiguation

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Using parallel corpora for word sense disambiguation

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