Automatically generating effective search queries directly from community question-answering questions for finding related questions

Figueroa, Alejandro

doi:10.1016/j.eswa.2017.01.041

Cited by 26 publications

(19 citation statements)

References 9 publications

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“…The fast popularity of Web 2.0 results in a great change in health area and leads to the emergence of Medicine 2.0 (Eysenbach, 2008;Van De Belt et al, 2010), which provides an interactive and effective communication platform for doctors and patients. More and more patients like to find health information and share their experience in online health communities (OHC), which is widely adopted in Medicine 2.0 (Figueroa, 2017;Roberts and Demner-Fushman, 2016). Many online health communities, such as HealthTap (www.healthtap.com) and GoodDoctor (www.haodf.com), provide the communication channel between doctors and patients.…”

Section: Introductionmentioning

confidence: 99%

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A study of similar question retrieval method in online health communities

Xing

Yin

Yan

et al. 2021

IJCS

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Purpose The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing. Design/methodology/approach This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics. Findings The experiment results show that the proposed method outperforms the baseline methods. Originality/value This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

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Section: Introductionmentioning

confidence: 99%

“…Before patients want to ask health questions, they will normally search for the similar questions and related answers at first. Quite a few questions have already been asked and answered in the past (Figueroa, 2017). It is time consuming for patients to browse each question and answer to locate information they interest.…”

Section: Introductionmentioning

confidence: 99%

A study of similar question retrieval method in online health communities

Xing

Yin

Yan

et al. 2021

IJCS

View full text Add to dashboard Cite

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“…As the NLP interface uses the knowledge graph based model and provides the semantic capability to the speech recognizer. [5] proposed a technique for finding allied queries from the question labels and its body for the community question answering knowledge bases. To accomplish the objectives he used NER (Named Entity Recognition) and NLP (Natural Language Processing) techniques and found that NER approach is the best in the optimization of the questions.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Computing based Question-Answering System for Teaching Electrical Motor Concepts

Prajapati¹,

Chaturvedi²

2019

IJCA

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Today is the era of "Big Data", and one has to spend ample amount of time to extract the meaningful information from such a huge store of data. It leads towards such a question answering system which can offer exact and precise answers to user queries. For that there is a requirement of understanding user queries effectively. Thus this paper proposes a cognitive computing powered question answering system in the field of education, which posses the power of Natural Language Processing (NLP). Here, cognitive computing provides the methods for synergism of several powers into a single architecture, NLP provides understanding of the user questions effectively, and Ontology endows with the techniques for the construction of robust knowledge base. So for the realistic implementation of the proposed architecture, the education domain has chosen and will be teaching electrical motor concepts to the edification of the students.[11] proposed a trigger and class based language modeling technique for getting better sentence retrieval method throughout the process of question answering. To confine the relationship in between different terms (words) they used class model. This method uses the similar concept as used in the clustering algorithm, where similar words go for the same cluster. This clustering is further useful for searching germane

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“…If the question is longer, it may be more clear, which may help users give a more relevant answer (Figueroa, 2017).…”

Section: Metadata-based Featuresmentioning

confidence: 99%

EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering

Xie¹,

Wang²,

Ma³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important types turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50%.

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Automatically generating effective search queries directly from community question-answering questions for finding related questions

Cited by 26 publications

References 9 publications

A study of similar question retrieval method in online health communities

A study of similar question retrieval method in online health communities

Cognitive Computing based Question-Answering System for Teaching Electrical Motor Concepts

EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering

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