Open Domain Question Answering via Semantic Enrichment

Sun, Huan; Ma, Hao; Yih, Wen-tau; Tsai, Cary Chi‐Liang; Liu, Jingjing; Chang, Ming‐Wei

doi:10.1145/2736277.2741651

Cited by 92 publications

(65 citation statements)

References 29 publications

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“…QuASE [129] is a three stage open domain approach based on web search and the Freebase knowledge base 11 . First, QuASE uses entity linking, semantic feature construction and candidate ranking on the input question.…”

Section: Systemsmentioning

confidence: 99%

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Survey on challenges of Question Answering in the Semantic Web

et al. 2017

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Abstract. Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher's time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

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Section: Systemsmentioning

confidence: 99%

“…Instead of querying a specific knowledge base, Sun et al [129] use web search engines to extract relevant text snippets, which are then linked to Freebase, where a ranking function is applied and the highest ranked entity is returned as the answer.…”

Section: Systemsmentioning

confidence: 99%

Survey on challenges of Question Answering in the Semantic Web

et al. 2017

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“…To test our framework we used TREC QA (Sun et al, 2015), WikiMovies (Miller et al, 2016) benchmarks and the new Yahoo! Answers dataset 2 derived from factoid questions posted on the CQA 2 available for research purposes at http://ir.mathcs.emory.edu/software-data/ website (Table 2).…”

Section: Experimental Evaluationmentioning

confidence: 99%

EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering

Savenkov

Agichtein

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answering. EviNets scores candidate answer entities by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their relevance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demonstrate the effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate future work on combining evidence signals for joint reasoning in question answering.

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“…Recent examples of such QA systems include PowerAqua [11], FREyA [6], QAKiS [5], and TBSL [23]. QuASE [21] is a corpus-based open domain QA system that mines answers directly from Web documents.…”

Section: Motivationmentioning

confidence: 99%

Qanary – A Methodology for Vocabulary-Driven Open Question Answering Systems

Both¹,

Diefenbach

Singh

et al. 2016

The Semantic Web. Latest Advances and New Domains

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Abstract. It is very challenging to access the knowledge expressed within (big) data sets. Question answering (QA) aims at making sense out of data via a simpleto-use interface. However, QA systems are very complex and earlier approaches are mostly singular and monolithic implementations for QA in specific domains. Therefore, it is cumbersome and inefficient to design and implement new or improved approaches, in particular as many components are not reusable. Hence, there is a strong need for enabling best-of-breed QA systems, where the best performing components are combined, aiming at the best quality achievable in the given domain. Taking into account the high variety of functionality that might be of use within a QA system and therefore reused in new QA systems, we provide an approach driven by a core QA vocabulary that is aligned to existing, powerful ontologies provided by domain-specific communities. We achieve this by a methodology for binding existing vocabularies to our core QA vocabulary without re-creating the information provided by external components. We thus provide a practical approach for rapidly establishing new (domain-specific) QA systems, while the core QA vocabulary is re-usable across multiple domains. To the best of our knowledge, this is the first approach to open QA systems that is agnostic to implementation details and that inherently follows the linked data principles.

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Open Domain Question Answering via Semantic Enrichment

Cited by 92 publications

References 29 publications

Survey on challenges of Question Answering in the Semantic Web

Survey on challenges of Question Answering in the Semantic Web

EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering

Qanary – A Methodology for Vocabulary-Driven Open Question Answering Systems

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