A language-independent LESK based approach to Word Sense Disambiguation (2016)

Abstract: This paper describes a language- independent LESK based approach to Word Sense Disambiguation (WSD), involving also Vector Space Models applied to the Distributional Semantics Hypotesis. In particular this approach tries to solve some issues that come up with less-resourced languages. The approach also addresses the inadequacy of the Most Frequent Sense (MFS) heuristics to fit specific domain corpora.

## introduction this language independent approach to wsd, even if in a very early stage of development, tries to solve two main problems. we use wordnet (wn) resources @xcite @xcite (synsets glosses and examples) from a specific standpoint: preferring to avoid the usage of monolingual resources, even though the specific task does not involve cross-lingual wsd on aligned parallel corpora. it has to be pointed out that the approach is being considered 'unsupervised': it does not rely on semantic annotation, although lemmatization and pos-tagging are taken into account. in most cases the quality of lexical resources is very variable, even though some languages have good resources, as of course english and, for instance, italian with both multiwordnet @xcite and italwordnet @xcite . an example is given with the dog/câine (first synset) glosses and examples foot_0 in assuming a wsd approach involving overlap counts, the english words canis, wolf, breeds will be counted in; as for the romanian words animal, mamifer, carnivor (all is-a relations), their english lemmas would be reached in any case in an expanded gloss implementation. anyway, pazȃ and vânȃtoare ('guarding' and 'hunting') would be useful for the same task. in the counterexample given in in general, it can be noticed how variable the quality is for different corpora and for different synsets. anyway, usually english wn provides the best and richest set of examples for a given synset. this variability in quality is observable also concerning the coverage of different wns 2 @xcite . ## methodology this approach is organized in two disambiguation steps. the first (focused on quality) is based mainly on a kind of lesk adapted in the languageindependent perspective discussed above and involves wn glosses and examples. the second (focused on quantity) is based on vsms and follows the assumption, coherent with the distributional hypothesis, that the neighbours of the target word in the semantic space are semantically related (in paradigmatic relations) with the target word. both these two disambiguation steps will be discussed in this section. ## data set all the experiments have been done on the italian multisemcor @xcite @xcite corpus, already sense-tagged. semcor is the perfect data set for this task, as it is the first case of corpus annotated with wn synsets and it is available in various languages (english, italian, romanian and japanese). the italian multisemcor contains 14,144 sentences and 261,283 tokens, 119,802 of which are annotated with senses. the availability in a good number of languages makes multisemcor a good resource to try this language-independent approach. also the ntu-multilingual corpus (ntu-mc) @xcite could be a perfect resource for this kind of experiments. ntu-mc is a corpus designed to be multilingual from the start. it contains parallel text in eight languages: english, mandarin chinese, japanese, indonesian, korean, arabic, vietnamese and thai. ## implementation and evaluation the two algorithms have been implemented as python scripts importing the nltk @xcite wn interface and the gensim @xcite word2vec @xcite library. at first, the two algorithms were implemented separately, achieving the results discussed in subsection 2.1.2 and subsection 2.2.2, then the two algorithms have been implemented together in sequence. the first algorithm has been used for a first disambiguation step excluding candidate synsets with scores lower than the 50% of the maximum, then the second algorithm has been applied taking into account only the remaining candidate synsets (provided by the first step of disambiguation) instead of considering all the possible synsets. when the candidate with higher score in the paradigmatic relations algorithm differs from the one with higher score in the lesk based one, the two scores are normalized in a minimummaximum 0-1, range and the candidate synset with the highest average is chosen. the results have improved a lot achieving an encouraging result: 38.67% of the content words have been correctly annotated, with a maximum number of 6 synsets for the context words. ## future work there is reason to hope that some further attempts based on the approach described in this paper will lead to significant improvements in languageindependent wsd. a first improvement will be exploiting the disambiguated glosses at least for english, as most of the english glosses are disambiguated. a second improvement will be the extension of the lesk based algorithm with other languages; considered that many glosses are translations of english, we should focus on merge wns (dutch, polish, etc) in particular. to do that it will be useful to extend nltk multilingual support: the .definition() and .examples() methods of wn synsets would be much more useful for tasks like this by exploiting a lang attribute. a third improvement will be a further development of scores definitions and a complete testing of parameters like: for the first algorithm, the lower bound for the candidate synsets to be saved and passed to the second step of disambiguation and the upper bound for the number of synsets of the context words; for the second algorithm, the number of neighbours or even try to include the approach defined by @xcite .