🤖 AI Summary
This work proposes a semantic similarity computation method that integrates Word Mover’s Distance (WMD) with pretrained word embeddings such as GloVe to better model the semantic relationship between queries and documents in information retrieval. Traditional centroid-based word embedding approaches often fail to capture fine-grained semantic matches, particularly when handling synonymy and polysemy. By minimizing the transportation cost of aligning query and document terms in the embedding space, the proposed method achieves a more precise representation of semantic correspondence. Experimental results demonstrate that this approach significantly outperforms baseline models—including Doc2Vec and Latent Semantic Analysis (LSA)—on similarity ranking tasks, while maintaining domain independence and high retrieval accuracy, thereby confirming its effectiveness and generalizability in practical information retrieval scenarios.
📝 Abstract
Search behaviour is characterised using synonymy and polysemy as users often want to search information based on meaning. Semantic representation strategies represent a move towards richer associative connections that can adequately capture this complex usage of language. Vector Space Modelling (VSM) and neural word embeddings play a crucial role in modern machine learning and Natural Language Processing (NLP) pipelines. Embeddings use distributional semantics to represent words, sentences, paragraphs or entire documents as vectors in high dimensional spaces. This can be leveraged by Information Retrieval (IR) systems to exploit the semantic relatedness between queries and answers. This paper evaluates an alternative approach to measuring query statement similarity that moves away from the common similarity measure of centroids of neural word embeddings. Motivated by the Word Movers Distance (WMD) model, similarity is evaluated using the distance between individual words of queries and statements. Results from ranked query and response statements demonstrate significant gains in accuracy using the combined approach of similarity ranking through WMD with the word embedding techniques. The top performing WMD + GloVe combination outperforms all other state-of-the-art retrieval models including Doc2Vec and the baseline LSA model. Along with the significant gains in performance of similarity ranking through WMD, we conclude that the use of pre-trained word embeddings, trained on vast amounts of data, result in domain agnostic language processing solutions that are portable to diverse business use-cases.