Research Area:  Machine Learning

Abstract:

Question Answering (QA) systems based on Information Retrieval return precise answers to natural language questions, extracting relevant sentences from document collections. However, questions and sentences cannot be aligned terminologically, generating errors in the sentence retrieval. In order to augment the effectiveness in retrieving relevant sentences from documents, this paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems. In detail, synonyms and hypernyms of relevant terms occurring in the question are first extracted from MultiWordNet and, then, contextualized to the document collection used in the QA system. Finally, the resulting set is ranked and filtered on the basis of wording and sense of the question, by employing a semantic similarity metric built on the top of a Word2Vec model. This latter is locally trained on an extended corpus pertaining the same topic of the documents used in the QA system. This QE approach is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain, assessing its effectiveness in retrieving sentences containing proper answers to questions belonging to four different categories.

Keywords:  

Author(s) Name:  Massimo Esposito, Emanuele Damiano, Aniello Minutolo, Giuseppe De Pietro, Hamido Fujita

Publisher name:  Elsevier

DOI:  10.1016/j.ins.2019.12.002

Volume Information:  Volume 514, April 2020, Pages 88-105

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0020025519311107