# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Comparing with Other Systems  
From Table 6, a short description for each system is given as follows (same as Table 3 in [15]). Wsdit is an unsupervised system using a Lesk-like similarity between the contexts of ambiguous words and dictionary definitions. Experiments were performed for various window sizes and various similarity measures. Cymfony is a maximum entropy model for unsupervised clustering and using nearby words and syntactic structures as features. A few annotated instances were used to map context clusters to WordNet/Worsmyth senses. Prob0 is a combination of two unsupervised modules using basic parts-of-speech tag and frequency information. Wsdw2v (our method) uses Word2 vec to construct a context sentence vector and sense signatures' sentence vectors retrieved from WordNet, computing the cosine similarity between those sentence vectors combined with the probability of sense distribution (using the average of Google word-embedding vectors, context = 10, similarity threshold \(\alpha\) = 0.37, \(n\_best\) = 1). Clr04-ls is an unsupervised system relying on definition properties (e.g., syntactic, semantic, subcategorization patterns, other lexical information), as given in a dictionary. Performance is generally a function of how well senses are distinguished. CIAOSENSO is an unsupervised system that combines the conceptual density idea with the frequency of words to disambiguate them. Information about domains was also taken into account. KUNLP is an algorithm that disambiguates the senses of a word by selecting a substituent among its WordNet relatives (e.g., antonyms KUNLP, hypernyms, etc.). The selection was made based on co-occurrence frequencies, measured on a large corpus. Duluth-SenseRelate is an algorithm that assigns the sense to a word that is most related to the possible senses of its neighbors, using WordNet glosses to measure relatedness between senses. DFA-LS-Unsup is a combination of three heuristics: the similarity between synonyms and the context, according to a mutual information measure; lexico-syntactic patterns extracted from WordNet glosses; and the first sense heuristic. DLSI-UA-LS-NOSU is an unsupervised method based on WordNet domains [40]; it exploits the information contained in glosses of WordNet domains and uses \"Relevant Domains\" obtained from the association ratio over domains and words. \begin{table}
\begin{tabular}{c c c} \hline \hline
**System/Team** & **Precision** & **Recall** \\ \hline wsdit/IIT Bombay ([PERSON] et al.) & 0.661 & 0.657 \\ Cymfony/([PERSON]) & 0.563 & 0.563 \\ the “most frequent sense” heuristic-baseline & 0.552 & 0.552 \\ Prob0/Cambridge U. ([PERSON]) & 0.547 & 0.547 \\ wsdw2v with sense distribution (our method) & 0.509 & 0.488 \\ wsdw2v without sense distribution (our method) & 0.487 & 0.466 \\ clr04-ls/CL Research ([PERSON]) & 0.450 & 0.450 \\ CIAOSENSO/U. Genova ([PERSON]) & 0.501 & 0.417 \\ LSA Lesk & 0.408 & 0.408 \\ KUNLP/Korea U. ([PERSON]) & 0.404 & 0.404 \\ Duluth-SenseRelate/U.Minnesota ([PERSON]) & 0.403 & 0.385 \\ Simplified Lesk ([PERSON] and [PERSON]) & 0.311 & 0.298 \\ Adapted Lesk ([PERSON] and [PERSON]) & 0.247 & 0.236 \\ DFA-LS-Unsup/UNED ([PERSON]) & 0.234 & 0.234 \\ DLSI-UA-LS-NOSU/U.Alicante ([PERSON]) & 0.197 & 0.117 \\ Original Lesk ([PERSON]) & 0.097 & 0.053 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of unsupervised systems participating in the SENSEVAL-3 English lexical sample task compared with our method and baselines. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Configurations** & **Precision** & **Recall** \\ \hline Summing word-embedding vectors & 0.417 & 0.399 \\ Average word-embedding vectors + idf & 0.416 & 0.398 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of different configurations for constructing a sentence vector. For the baselines, \"most frequent sense\" heuristic baseline is known as a very hard baseline to outperform by unsupervised WSD algorithms. The original Lesk computes overlapping words between each pair of word sense definitions.