Question Analysis Module Research Articles

Document Retrieval System for Biomedical Question Answering

In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity.

Improving Question Analysis for Arabic Question Answering in the Medical Domain

Question analysis is a basic module in a question answering (QA) system, and its quality affects the performance of QA system. In this paper, we address the problem of Arabic question analysis in the medical domain where several specific challenges are met. The major challenging issue in processing Arabic medical question is the need for ambiguity resolution. Nevertheless, this issue has not been well studied in related works. Our question analysis uses dictionaries and transducers to analyze any medical question, factoid or complex. This module detects important elements of the question, including: different words in the question that identify what the user wants to ask for, and the nature of the expected answer. To identify well these elements a step of disambiguation is applied. Then, the words used in the question will be extended by adding new words that connect semantically to those in the question. Experimentation of the question analysis module of our Arabic medical question answering system show interesting results.

Question Generation for French: Collating Parsers and Paraphrasing Questions

This article describes a question generation system for French. The transformation of declarative sentences into questions relies on two different syntactic parsers and named entity recognition tools. This makes it possible to further diversify the questions generated and to possibly alleviate the problems inherent to the analysis tools. The system also generates reformulations for the questions based on variations in the question words, inducing answers with different granularities, and nominalisations of action verbs. We evaluate the questions generated for sentences extracted from two different corpora: a corpus of newspaper articles used for the CLEF Question Answering evaluation campaign and a corpus of simplified online encyclopedia articles. The evaluation shows that the system is able to generate a majority of good and medium quality questions. We also present an original evaluation of the question generation system using the question analysis module of a question answering system.

단어 의미 정보를 활용하는 이용자 자연어 질의 유형의 효율적 분류

For question-answering system, question analysis module finds the question points from user`s natural language questions, classifies the question types, and extracts some useful information for answer. This paper proposes a question type classifying technique based on focus words extracted from questions and word semantic information, instead of complicated rules or huge knowledge resources. It also shows how to find the question type without focus words, and how useful the synonym or postfix information to enhance the performance of classifying module.