# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 1 Introduction  
Word sense disambiguation (WSD) is an important and challenging task for natural language processing (NLP) applications like in machine translation, information retrieval, question answering, speech synthesis, sentiment analysis, etc. The main goal of WSD is to take an ambiguous word in context and a set of possible meanings (or senses) and try to identify the sense of this ambiguous word. There are two types of WSD tasks: Sample tasks and all-words tasks. A sample task tries to disambiguate pre-selected words given an inventory of senses for each word. This leads to a supervised approach by training a word-specific classifier to disambiguate a target word using a training corpus. This training corpus requires each context to be manually annotated with a correctsense [1]. An all-words task tries to disambiguate all words in the entire text, which is difficult to provide in an all-words training corpus. This leads to an unsupervised approach which discovers underlying semantic and syntactic relations in the large text corpus and also works with a dictionary/lexicon to determine the correct sense [2; 3; 4; 5; 6]. Besides this, there is also a semi-supervised approach which only has a small set of contexts annotated with a correct sense and uses a bootstrapping method to assign correct senses to unlabeled contexts gradually. This work was earlier introduced by [PERSON] [7]. The unsupervised approach has the advantage of no needing a huge annotated corpus being more practical in the real-world applications. This can be done with the help of a large, well-organized public lexicon known as WordNet [8], which provides a set of cognitive synonyms (or synsets) for each word categorized by part-of-speech tag where each synset comes with a definition (or gloss), examples of usage, and its relationship to other words. WordNet does not provide directly a set of senses for each word but a set of synsets where each synset represents a concept/sense. In our experiments, we used WordNet as the lexicon to determine the correct sense. For example, synsets of \"cake\" are listed in Table 1. Moreover, the concept of the sense is expanded with its synset relationship such as hypernym, hyponym, meronym, holonym, etc., except for antonym relation. This expansion is called the signature of the sense. Intuitively, the correct sense of the target word tends to have high similarity between its sense definition and the context. The traditional approaches (i.e., simplified Lesk [4] and adapted Lesk [5]) computed similarity from overlapping words between sense definition and its context, which must match exactly. These approaches failed to determine similarity for WSD. Latent semantic analysis (LSA) [9] aided in this problem by changing from an overlap-based approach to a vector-based approach and analyzing distributional semantic relationships between a set of documents and terms in documents, then using mathematical concepts of vectors to determine similarity as in enhanced Lesk [6]. In the big data era, the size of the text corpus becomes larger and larger. In order to capture semantic and syntactic word similarities underlying a huge corpus of text, the counter-based approach like LSA would be impracticable to implement because it consumes much more memory and is unable to update the model with the additional text corpus without re-training from the beginning. Therefore, the prediction-based approach, Word2 vec [10] has the advantage in these issues [11; 12; 13]. Word2 vec is a popular word-embedding approach that represents each word on the fixed-size vector space model through either the skip-gram or continuous bag-of-words (CBOW) model trained by one hidden layer artificial neural network which effectively capturing semantic and syntactic word similarities from a huge corpus of text. In our experiments, we used Word2 vec to map each word with a corresponding word-embedding vector to construct in terms of both sense signature and the context to the sentence vector in various configurations.