Question Analysis for Arabic Question Answering Systems

TL;DR

This paper presents a comprehensive question analysis framework for Arabic QA systems, utilizing linguistic tools and classifiers to identify key question elements and improve answer retrieval accuracy.

Contribution

It introduces a detailed Arabic question analysis approach combining multiple NLP techniques and detection rules to enhance question understanding in QA systems.

Findings

Improved question element detection accuracy.

Enhanced answer ranking performance using detailed analysis.

Effective integration of linguistic tools for Arabic questions.

Abstract

The first step of processing a question in Question Answering(QA) Systems is to carry out a detailed analysis of the question for the purpose of determining what it is asking for and how to perfectly approach answering it. Our Question analysis uses several techniques to analyze any question given in natural language: a Stanford POS Tagger & parser for Arabic language, a named entity recognizer, tokenizer,Stop-word removal, Question expansion, Question classification and Question focus extraction components. We employ numerous detection rules and trained classifier using features from this analysis to detect important elements of the question, including: 1) the portion of the question that is a referring to the answer (the focus); 2) different terms in the question that identify what type of entity is being asked for (the lexical answer types); 3) Question expansion ; 4) a process of classifying the question into one or more of several and different types; and We describe how these elements are identified and evaluate the effect of accurate detection on our question-answering system using the Mean Reciprocal Rank(MRR) accuracy measure.