# Word Sense Disambiguation Using Clustered Sense Labels
## 1 Introduction  
In computational semantic analysis, word sense disambiguation (WSD), i.e., finding the correct sense of a word in a given context, has long been a challenge in natural language understanding. Recently, it has mostly been studied with machine learning models typically using the supervised, unsupervised, and knowledge-based approaches [1; 2; 3; 4]. The supervised approach [5; 6] uses a sense annotated corpus to train the models and usually suffers from data sparseness. The unsupervised approach [7; 8] uses raw text to find a word cluster with the same sense, discriminating it from others. However, these senses often do not entirely match those defined in a dictionary or a desaurus; therefore, clustering such senses for explainable applications is not trivial. The knowledge-based approach [9; 10] uses glossary information, usually from a dictionary or thesaurus, to match with the context of a target word. The glossary information is usually insufficient to cover all the contexts of a target word. To date, supervised learning becomes popular and is considered an effective approach, but suffers from processing unknown senses in the training data. To alleviate this problem, the supervised approach combined with the knowledge-based approach has recently been researched. Advances in computing power enables the various WSD algorithms to utilize the large size of dictionary and corpus [11]. Word embeddings and contextual word vectors are especially used widely based on neural network models in a pre-trained manner [12; 13; 14]. [PERSON] et al. [15] used the sense definition in a glossary to match the context of a target word by using the sentence pair comparison function of BERT [16]. The context vector and the glossary vector are trained in a supervised way for fine-tuning of a generic BERT system. [PERSON] et al. [17] extended this idea to include thesaurus information into the sense definition and merge them for continuous vector representation. However, this approach requires a large amount of memory to process all the senses of homographs and their related glossaries. Moreover, when decoding it requires multiple scans for the homographs' sense glossaries. The sense vocabulary compression (SVC) method tackles the unknown sense problem differently by compressing sense tags [18]. It finds the optimal set of sense tags bydecreasing the number of sense tags using sense relations, such as synonyms, hyperyms, and hyponyms in a thesaurus. The synonyms are simply compressed to one representative sense, known as the synset tag. The hypernym and hyponym sense tags are compressed by using the highest common ancestor tag not shared with the other senses in the homograph. Figure 1 shows an example of compressed sense vocabularies in hypernym hierarchical relations. There are two kinds of homographs in the figure: _fund_ and _bank_. The dotted rectangles show the maximum subtrees not shared with the other senses in the homographs. Each root in the subtrees is a representative sense. For example, _nature_#1 is a representative sense of _bank_#7 and _river_#1; _financial institution_#1 is that of _fund_#1 and _bank_#4. The compressed sense vocabulary achieves engineering efficiency in processing smaller output parameters and learning efficiency for sparse or unknown sense tags to be included implicitly during the learning phase. Once the compressed sense vocabulary is obtained, no additional memory or multiple scans are required for decoding; therefore, it is extremely compact and fast. However, for sense compression, the SVC method requires a superior quality thesaurus. Although there are thesaurus for many languages, most are not rich enough or freely available [19]. In this paper, we propose an alternative sense vocabulary compression method without using a thesaurus. Instead, the sense definitions in a dictionary become the embedding vectors and are clustered to find representative sense vocabularies, which are the compressed sense vocabularies.