Leveraging Sentiment to Compute Word Similarity
This work addresses a specific problem in natural language processing for tasks requiring nuanced word similarity, but it is incremental as it builds on existing WordNet metrics by adding sentiment.
The authors tackled the problem of measuring word similarity by incorporating sentiment information, proposing SenSim, a WordNet-based metric that uses sentiment scores from glosses and achieves better annotator agreement when sentiment is considered.
In this paper, we introduce a new WordNet based similarity metric, SenSim, which incorporates sentiment content (i.e., degree of positive or negative sentiment) of the words being compared to measure the similarity between them. The proposed metric is based on the hypothesis that knowing the sentiment is beneficial in measuring the similarity. To verify this hypothesis, we measure and compare the annotator agreement for 2 annotation strategies: 1) sentiment information of a pair of words is considered while annotating and 2) sentiment information of a pair of words is not considered while annotating. Inter-annotator correlation scores show that the agreement is better when the two annotators consider sentiment information while assigning a similarity score to a pair of words. We use this hypothesis to measure the similarity between a pair of words. Specifically, we represent each word as a vector containing sentiment scores of all the content words in the WordNet gloss of the sense of that word. These sentiment scores are derived from a sentiment lexicon. We then measure the cosine similarity between the two vectors. We perform both intrinsic and extrinsic evaluation of SenSim and compare the performance with other widely usedWordNet similarity metrics.