Siamese Based Network for Detecting Quora Questions Similarity

• 영문명: Siamese Based Network for Detecting Quora Questions Similarity
• 발행기관: 한국컴퓨터게임학회
• 저자명: 이암(Yan LI)
• 간행물 정보: 『한국컴퓨터게임학회논문지』제35권 4호, 57~68쪽, 전체 12쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 초록

Quora search engine redirects to different discussion pages based on the search terms searched by the user. So, when questions that are semantically similar are searched on Quora, it sometimes redirects a user to different discussion pages even if there exists a page to the dedicated search. In such a case, Semantic Similarity among the questions carries highest weightage. So, for text, using traditional methods for calculating similarity, usually the text is considered as sequence of words and they just count the number of words that occurred in a sentence, on which some distance measures are applied to find the similarity, while missing the semantic level knowledge of the text during calculation. Considering such traditional methods, it will also require a huge training set as well as time to produce an accurate model. But in this Research Paper, Siamese Based Network is used that can train itself on a single example of each text to provide an accurate similarity output.I have used different types of pre-trained word embedding models like word-2-vec and glove to understand the semantics of the question pairs present in Quora Question Pair dataset. This paper introduces a new approach to calculate sentence similarity and gives astronishing results outperforming the current state of art Siamese Based LSTM models. Along with this new approach of using Manhattan LSTM with attention mechanism for similarity calculation, a comparative analysis is performed on the embedded question pairs, among different Siamese based LSTM models like LSTM and Manhattan LSTM, to predict whether the questions are similar or not and get the best model combination for Quora Question Pair

목차

1. Deep Learning and Text Similarity

2. Related Research

3. Research Methodology

4. Analysis and Evaluation

5. Conclusion

References

키워드

Siamese Neural Network LSTM Word Embedding Attention Mechanism Text Similarity Quora