When the answer comes into question in question-answering: survey and open issues

Mendes, Ana Cristina; Coheur, Luísa

doi:10.1017/s1351324911000350

Cited by 9 publications

(6 citation statements)

References 54 publications

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“…Another promising direction is to apply summarization techniques (Tigelaar et al 2010), which could be useful both for the model and when presenting the results to the user. This might involve determining which among several good answers to the target question is the most cooperative one, i.e., correct, useful, and non-misleading at the same time (Mendes and Coheur 2013).…”

Section: Resultsmentioning

confidence: 99%

Arabic community question answering

Nakov¹,

Màrquez²,

Moschitti³

et al. 2018

Nat. Lang. Eng.

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We analyze resources and models for Arabic community Question Answering (cQA). In particular, we focus on CQA-MD, our cQA corpus for Arabic in the domain of medical forums. We describe the corpus and the main challenges it poses due to its mix of informal and formal language, and of different Arabic dialects, as well as due to its medical nature. We further present a shared task on cQA at SemEval, the International Workshop on Semantic Evaluation, based on this corpus. We discuss the features and the machine learning approaches used by the teams who participated in the task, with focus on the models that exploit syntactic information using convolutional tree kernels and neural word embeddings. We further analyze and extend the outcome of the SemEval challenge by training a meta-classifier combining the output of several systems. This allows us to compare different features and different learning algorithms in an indirect way. Finally, we analyze the most frequent errors common to all approaches, categorizing them into prototypical cases, and zooming into the way syntactic information in tree kernel approaches can help solve some of the most difficult cases. We believe that our analysis and the lessons learned from the process of corpus creation as well as from the shared task analysis will be helpful for future research on Arabic cQA.

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Section: Resultsmentioning

confidence: 99%

Arabic community question answering

Nakov¹,

Màrquez²,

Moschitti³

et al. 2018

Nat. Lang. Eng.

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“…The goal of an open-domain QA system is to automatically return an answer for every natural language question [21,63,31]. For example, such systems as START [23], FAQFinder [8] and AnswerBus [68] answer questions over the Web.…”

Section: Open-domain Question Answeringmentioning

confidence: 99%

Ripple Down Rules for question answering

Nguyen

Pham

2017

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Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users' queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.

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“…A number of efforts have been made to solve the final answer in question-answering [11]. To the best of our knowledge, the process of answer solving is often divided into three phases [12].…”

Section: Related Workmentioning

confidence: 99%

A PUT-Based Approach to Automatically Extracting Quantities and Generating Final Answers for Numerical Attributes

Liu

Wang

Chen

et al. 2016

Entropy

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Abstract:Automatically extracting quantities and generating final answers for numerical attributes is very useful in many occasions, including question answering, image processing, human-computer interaction, etc. A common approach is to learn linguistics templates or wrappers and employ some algorithm or model to generate a final answer. However, building linguistics templates or wrappers is a tough task for builders. In addition, linguistics templates or wrappers are domain-dependent. To make the builder escape from building linguistics templates or wrappers, we propose a new approach to final answer generation based on Predicates-Units Table (PUT), a mini domain-independent knowledge base. It is deserved to point out that, in the following cases, quantities are not represented well. Quantities are absent of units. Quantities are perhaps wrong for a given question. Even if all of them are represented well, their units are perhaps inconsistent. These cases have a strong impact on final answer solving. One thousand nine hundred twenty-six real queries are employed to test the proposed method, and the experimental results show that the average correctness ratio of our approach is 87.1%.

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When the answer comes into question in question-answering: survey and open issues

Cited by 9 publications

References 54 publications

Arabic community question answering

Arabic community question answering

Ripple Down Rules for question answering

A PUT-Based Approach to Automatically Extracting Quantities and Generating Final Answers for Numerical Attributes

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