Bridging the vocabulary gap between questions and answer sentences

Momtazi, Saeedeh; Klakow, Dietrich

doi:10.1016/j.ipm.2015.04.005

Cited by 14 publications

(4 citation statements)

References 48 publications

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“…In the inside sentence triggering approach, the words occurring in the same sentence trigger each other while in the across sentence approach, words occurring in two consecutive sentences trigger each other. Momtazi and Klakow (2015) introduced three other triggering approaches namely, self‐triggering, question and answer pair triggering, and named entity triggering. In the self‐triggering model, each word just triggers itself and it is similar to the basic unigram model.…”

Section: Question Answer Similarity From Ir Perspectivementioning

confidence: 99%

Text‐based question answering from information retrieval and deep neural network perspectives: A survey

Abbasiantaeb

Momtazi

2021

WIREs Data Min & Knowl

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Text‐based question answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval (IR) techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning (DL) approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between the questions and the answer sentences. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both a traditional IR perspective and a more recent deep neural network environment. We also introduce well‐known datasets for the task and present available results from the literature to have a comparison between different techniques. This article is categorized under: Algorithmic Development > Text Mining Technologies > Machine Learning

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Section: Question Answer Similarity From Ir Perspectivementioning

confidence: 99%

Text‐based question answering from information retrieval and deep neural network perspectives: A survey

Abbasiantaeb

Momtazi

2021

WIREs Data Min & Knowl

Self Cite

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“…There are different ways to frame the task of answering questions by machine reading, including sentence retrieval (Momtazi and Klakow, 2015), multi-hop reasoning (Khot et al, 2020), and reasoning about multiple paragraphs or documents at the same time (Dua et al, 2019;Cao et al, 2019). Recent work has considered the development of reasoning-based QA systems (Weber et al, 2019) as well as the integration of external (Banerjee and Baral, 2020) and commonsense knowledge tured knowledge graphs.…”

Section: Related Workmentioning

confidence: 99%

BiQuAD: Towards QA based on deeper text understanding

Grimm¹,

Cimiano

2021

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

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Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question. Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained. We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion. The dataset consist of 4, 190 closed-domain texts and a total of 99, 149 question-answer pairs. The texts are synthetically generated soccer match reports that verbalize the main events of each match. All texts are accompanied by a structured Datalog program that represents a (logical) model of its information. We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset. In particular, transformer based state-of-theart models achieve F 1 -scores of only 39.0. We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.

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“…The POS-Tagger splits each sentence into nouns, verbs, adjectives and adverbs in the document. From the extracted split words, the stem words and noun words are taken into account for indexing, domain grouping and categorization for faster cluster formation [11]. The keyword can be extracted using the empirical formula given below (1):…”

Section: Algorithm 1: Algorithm For Pos-tagger-based Question Patternmentioning

confidence: 99%

A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet

Karpagam¹,

Saradha²

2019

Sādhanā

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The question answering system plays an important role in information retrieval field, where the user is in need of getting a precise answer instead of large collections of documents. The aim of this paper is to investigate techniques for improving sentence-based question answering system. To achieve this, a POS-Taggerbased question pattern analysis model is proposed to identify question type based on pattern template for the user-submitted query. Next, the knowledge base is created from a large corpus by clustering the documents by grouping on domain context. The proposed semantic-word-based answer generator model deals with the user query mapping with an appropriate sentence in the knowledge base. By the proposed models, the system reduces the search gap among user queries and answer sentences using Wordnet. It considers word order, overlap, sentence similarity, string distance, unambiguous words and semantic similarity of words. The proposed algorithm evaluates with benchmark datasets such as 20Newsgroup and TREC-9 QA, and proves its efficiency by statistical test for significance.

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Bridging the vocabulary gap between questions and answer sentences

Cited by 14 publications

References 48 publications

Text‐based question answering from information retrieval and deep neural network perspectives: A survey

Text‐based question answering from information retrieval and deep neural network perspectives: A survey

BiQuAD: Towards QA based on deeper text understanding

A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet

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