A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation (2020)

Abstract: Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks. In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses. Experiments on all-words and lexical sample datasets show that the proposed system achieves new state-of-the-art results, defeating previous knowledge-based systems by at least 5.5 F1 measure. When the system utilizes sense embeddings learned from SemCor, it outperforms all previous supervised systems with only 20% SemCor data.

## introduction word sense disambiguation (wsd) is an ongoing research area in natural language processing community. it is aimed at determining the correct meaning (sense) of a word in its context given a list of potential or competing senses in a sense inventory. according to @xcite , most of wsd solutions can be categorized into supervised and knowledge-based approaches. for supervised systems, they rely on sense-annotated data to train either word experts @xcite or a neural language model @xcite for disambiguation and thus perform better than their * corresponding author knowledge-based counterparts @xcite @xcite , which merely utilize sense knowledge in a sense inventory. however, knowledge-based approaches can better scale to a multilingual scenario or a specific domain where sense annotation is limited. contextual representations learned from neural language models @xcite are proved to be beneficial to the task of wsd. many recent systems @xcite @xcite utilize language models, especially @xcite , as a feature extraction tool to obtain contextual sense representations and outperform previous approaches by large margins. there are also systems @xcite that incorporate sense definitions into language models and achieve state-of-the-art performance. however, most of the systems are implemented in a supervised manner using a widely exploited sense-annotated corpus, semcor @xcite , and merging knowledge from the sense inventory as a supplement. there is much space to explore regarding how to better exploit knowledge in a sense inventory such as different wordnet relations and super-sense that categorizes wordnet senses into 45 clusters. in this paper, we present sref, a knowledgeenhanced wsd approach that effectively exploits the sense definitions and relations in an inventory. first, we design a gloss augmentation for those synsets that have a short definition in wordnet so that each synset can learn a reliable sense embedding with features from bert. then, based on these embeddings, we explore the contribution of different synset relations in wordnet @xcite to learn relation-enhanced sense embeddings. after the first wsd is conducted with a nearest neighbor approach against an ambiguous word's context embedding and the relation-enhanced embeddings of the word's potential senses, we implement a try-again mechanism to the top 2 competing senses using synset relations and super-sense category. when applying the proposed strategy to tackle wsd, our system achieves state-of-the-art performance among knowledge-based systems. when we concatenate our sense embeddings with those learned from semcor, new state-of-the-art performance in supervised category is achieved. we thus summarize our contributions as follows: to the best of our knowledge, this is the first attempt on employing wordnet relations to implement wsd again with sense relation knowledge. (3) state-of-the-art performance is achieved in both all-words and lexical sample wsd datasets, surpassing previous systems by 5.5 f1 measure in knowledge-based all-words wsd. the supervised version of our system achieves state-of-the-art performance with only 20% semcor data. the source code is available at: github.com/lwmlyy/sref. ## related work in order to tackle wsd, approaches in two streams have been well developed over the last few decades, namely supervised and knowledgebased approaches. their major difference is whether a sense-annotated corpus is employed. ## preliminaries in this section, we introduce wordnet and bert, the contextual representation learning model. ## method figure 1 demonstrates the overall concept of the framework without the try-again mechanism using an example. it relies on a k-nn algorithm to predict the correct sense of each word under disambiguation. the algorithm is implemented against a context representation ( , lighter grey circle) directly from bert at the position of the word under disambiguation and a knowledge-enhanced representation ( , smaller blue circle) from bert and wordnet knowledge. the big blue circle briefly illustrates how related senses are merged into one specific sense (section 4.2). in this big circle, the grey circles are basic sense embeddings ( , grey circle) learned from the synset's augmented gloss (section 4.1) via bert. ## experiment setup in this section, we evaluate our system using the evaluation framework provided by @xcite . this framework includes five standard all-words wsd datasets: senseval-2 (se2, @xcite , senseval-3 (se3, @xcite , semeval-2007 (se07, @xcite , semeval-2013 (se13, @xcite and semeval-2015 (se15, @xcite . we also show how our system performs on lexical sample datasets including senseval-2 (se2-ls, kilgarriff, 2001) and senseval-3 (se3-ls, @xcite . we use the preprocessed datasets from ufsac @xcite . ## results in this section, an ablation study is first implemented to illustrate how the proposed factors contribute to the final wsd performance and a test set example is given regarding the tryagain mechanism. then, we compare our systems' performance on all-words and lexical sample datasets with state-of-the-art systems. also, we demonstrate how the number of labeled sentences in semcor affects the performance of sref sup and lmms. finally, we experiment on how the knowledge-enhanced sense embeddings can benefit several similarity-calculating and ranking tasks with simple attempts. ## conclusion we have introduced sref, a synset relationenhanced framework with a try-again mechanism that takes into account wordnet relations and augments wordnet glosses with mentions from the web under simple hypotheses and rules. empirical experiments have proved the effectiveness of sref from both knowledgebased and supervised perspectives, obtaining major and minor improvements over previous state-of-the-art performance, respectively. for future work, we intend to scale srefkb to a multilingual version and explore the possibilities of using the multilingual wordnet so that abundant knowledge regarding english can be transferred to other languages. it is also worth investigating regarding how to better incorporate sense embedding into other downstream tasks.