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Exploring Semantic Similarity Measure Based on Word Embedding Representation for Arabic Passages Retrieval by Imane Lahbari; Said Ouatik El Alaoui is a book available to read on EtoBox.

What is Exploring Semantic Similarity Measure Based on Word Embedding Representation for Arabic Passages Retrieval about?

Passages Retrieval is a challenging task that directly affects the performance of the whole Arabic Question Answering Systems. Text representation is a fundamental process in many text mining application methods. Due to their ability to capture semantic meanings of vocabulary terms, Word Embeddings (WE) vector representations have shown their effectiveness for Natural Language Processing (NLP) tasks, especially for information Retrieval and Question Answering Systems. In this paper, we explore Arabic passages Retrieval based on both words embedding vector representation and Semantic Similarity Measures. First, we extract candidate passages from the obtained text document (Search Engine). Then, we compute vector representations for the question text of all generated passages using pre-trained models including Word2vec, GloVe and FastText. Eventually, we compute the semantic similarity between the question and the retrieved passage representations, in order to get the N top-ranked passages that are going to serve as answer candidates. We conduct several experiments using TREC and CLEF questions and retrieved passages from Wikipedia documents; we evaluate the performance using the ran

Author

Imane Lahbari; Said Ouatik El Alaoui

Publisher

Springer International Publishing : Imprint: Springer

Published

2022

Language

EN

ISBN

9783030906399

Subjects

Engineering, Stem