Sentence Similarity Learning by Lexical Decomposition and Composition (1602.07019v2)

Abstract: Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.

• The paper introduces a framework that decomposes word representations into similar and dissimilar components to capture nuanced semantic relationships.
• It employs a composition technique that aggregates these lexical components into a refined sentence similarity score.
• The approach demonstrates enhanced performance in semantic similarity tasks, indicating practical benefits for natural language processing applications.

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