Question answering in conversations: Query refinement using contextual and semantic information

Habibi, Maryam; Mahdabi, Parvaz; Popescu-Belis, Andréi

doi:10.1016/j.datak.2016.06.003

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Context plays a significant role in DialogueCRN [11], context reasoning [13], contextual query refinement [27], and contextual knowledge based query answering [9] systems. Only in the recently proposed ConteLog [12] was context modeled by giving it a syntax and a direct semantic.…”

Section: Context As a First-class Citizen For Query Answeringmentioning

confidence: 99%

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

Jamil

2023

Preprint

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In conversational query answering systems, context plays a significant role in accurately and meaningfully carrying it forward. In many chatbots, such as in Expedia, the discussion quickly degenerates into circling back to restarting the conversation or to inviting a live agent to intervene because the bot could not grasp the context. Contexts shorten interactions by way of implied query constraints to narrow search and to not repeat them in subsequent queries. In this paper, we introduce a novel way of viewing contexts as a distance function via the concept of query relaxation. We demonstrate that a typed domain distance function is sufficient to model context in a conversation. Our approach is based on the idea of non-monotonic constraint inheritance in a context hierarchy.

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Section: Context As a First-class Citizen For Query Answeringmentioning

confidence: 99%

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

Jamil

2023

Preprint

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“…Some of the works in the literature analyze different stages in a QA framework. Habibi, Mahdabi and Popescu-Belis (2016) focus on query expansion for a conversational environment. They extract the question context using a conversation fragment and identify the important keywords in the context with a topic similarity metric.…”

Section: Related Workmentioning

confidence: 99%

A closed-domain question answering framework using reliable resources to assist students

Derici¹,

Aydın²,

Yenialaca³

et al. 2018

Nat. Lang. Eng.

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This paper describes a question answering framework that can answer student questions given in natural language. We suggest a methodology that makes use of reliable resources only, provides the answer in the form of a multi-document summary for both factoid and open-ended questions, and produces an answer also from foreign resources by translating into the native language. The resources are compiled using a question database in the selected domains based on reliability and coverage metrics. A question is parsed using a dependency parser, important parts are extracted by rule-based and statistical methods, the question is converted into a representation, and a query is built. Documents relevant to the query are retrieved from the set of resources. The documents are summarized and the answers to the question together with other relevant information about the topic of the question are shown to the user. A summary answer from the foreign resources is also built by the translation of the input question and the retrieved documents. The proposed approach was applied to the Turkish language and it was tested with several experiments and a pilot study. The experiments have shown that the summaries returned include the answer for about 50–60 percent of the questions. The data bank built for factoid and open-ended questions in the two domains covered is made publicly available.

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“…•(Kim et al, 2017) •(Fernández-Reyes et al, 2018) •(Stein et al, 2019) •(Othman et al, 2017) •(Habibi et al, 2016) …”

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Query Sense Discovery Approach to Realize the User's Search Intent

Chenaina

Neji

Shoeb

2021

International Journal of Information Retrieval Research

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The main goal of information retrieval is getting the most relevant documents to a user’s query. So, a search engine must not only understand the meaning of each keyword in the query but also their relative senses in the context of the query. Discovering the query meaning is a comprehensive and evolutionary process; the precise meaning of the query is established as developing the association between concepts. The meaning determination process is modeled by a dynamic system operating in the semantic space of WordNet. To capture the meaning of a user query, the original query is reformulating into candidate queries by combining the concepts and their synonyms. A semantic score characterizing the overall meaning of such queries is calculated, the one with the highest score was used to perform the search. The results confirm that the proposed "Query Sense Discovery" approach provides a significant improvement in several performance measures.

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Question answering in conversations: Query refinement using contextual and semantic information

Cited by 8 publications

References 23 publications

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

A closed-domain question answering framework using reliable resources to assist students

Query Sense Discovery Approach to Realize the User's Search Intent

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