Sentence Similarity Techniques for Short vs Variable Length Text using Word Embeddings

Authors

  • D. Shashavali Samsung R & D Bangalore
  • V. Vishwjeet Samsung R & D Bangalore
  • Rahul Kumar Samsung R & D Bangalore
  • Gaurav Mathur Samsung R & D Bangalore
  • Nikhil Nihal Samsung R & D Bangalore
  • Siddhartha Mukherjee Samsung R & D Bangalore
  • Suresh Venkanagouda Patil Samsung R & D Bangalore

DOI:

https://doi.org/10.13053/cys-23-3-3273

Keywords:

Sentence similarity, word embeddings, natural language processing, sliding window, N-grams, text classification

Abstract

In goal-oriented conversational agents like Chatbots, finding the similarity between user input and representative text result is a big challenge. Generally, the conversational agent developers tend to provide a minimal number of utterances per intent, which makes the classification task difficult. The problem becomes more complex when the length of the representative text per action is short and the length of the user input is long. We propose a methodology that derives Sentence Similarity score based on N-gram and Sliding Window and uses the FastText Word Embeddings technique which outperforms the current state-of-the-art Sentence Similarity results. We are also publishing a dataset on the shopping domain, to build conversational agents. And the extensive experiments done on the dataset fetched better results in accuracy, precision and recall by 6%, 2% and 80% respectively. It also evinces that our solution generalizes well on the low corpus and requires no training.

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Published

2019-09-25

Issue

Vol. 23 No. 3 (2019): Thematic issue: Computational Linguistics (Guest Editor A. Gelbukh)

Section

Articles of the Thematic Issue

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