Question Answering By Predictive Annotation

Prager, John; Chu-Carroll, Jennifer; Brown, Eric W.; Czuba, Krzysztof

doi:10.1007/978-1-4020-4746-6_10

Cited by 56 publications

(75 citation statements)

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“…This approach is different from the other reranking methods in that it does not rearrange the order of the retrieved passages, but it eliminates retrieved passages that are likely to not contain an answer based on candidate named entity matches, acting similarly to a passage answer type filter in other QA systems [4]. This approach is interesting for comparing with our reranking methods Atype-DP and Atype-DP-IP that involve an analysis of parse structures.…”

Section: Elimination Of Non-answer-type-bearing Passages (Quan-elim)mentioning

confidence: 99%

Passage Reranking for Question Answering Using Syntactic Structures and Answer Types

Aktolga

Allan

Smith

2011

Lecture Notes in Computer Science

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Abstract. Passage Retrieval is a crucial step in question answering systems, one that has been well researched in the past. Due to the vocabulary mismatch problem and independence assumption of bag-of-words retrieval models, correct passages are often ranked lower than other incorrect passages in the retrieved list. Whereas in previous work, passages are reranked only on the basis of syntactic structures of questions and answers, our method achieves a better ranking by aligning the syntactic structures based on the question's answer type and detected named entities in the candidate passage. We compare our technique with strong retrieval and reranking baselines. Experimental results using the TREC QA 1999-2003 datasets show that our method significantly outperforms the baselines over all ranks in terms of the MRR measure.

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Section: Elimination Of Non-answer-type-bearing Passages (Quan-elim)mentioning

confidence: 99%

Passage Reranking for Question Answering Using Syntactic Structures and Answer Types

Aktolga

Allan

Smith

2011

Lecture Notes in Computer Science

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“…In particular, this work has been framed as research on thread resolveability in QA sites. It can be conceived as the human counterpart to fully automated question answering systems (Prager et al, 2000;Perera, 2012;Jeon et al, 2006;Agichtein et al, 2008). Much of this work has emphasized the importance of having effective features to model question and answer processes.…”

Section: Related Workmentioning

confidence: 99%

Towards Identifying the Resolvability of Threads in MOOCs

Yang

Wen

Rosé

2014

Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs

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One important function of the discussion forums of Massive Open Online Courses (MOOCs) is for students to post problems they are unable to resolve and receive help from their peers and instructors. There are a large proportion of threads that are not resolved to the satisfaction of the students for various reasons. In this paper, we attack this problem by firstly constructing a conceptual model validated using a Structural Equation Modeling technique, which enables us to understand the factors that influence whether a problem thread is satisfactorily resolved. We then demonstrate the robustness of these findings using a predictive model that illustrates how accurately those factors can be used to predict whether a thread is resolved or unresolved. Experiments conducted on one MOOC show that thread resolveability connects closely to our proposed five dimensions and that the predictive ensemble model gives better performance over several baselines.

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“…If a parse of the query fails, then the system is still able to find answer sentences based on robust techniques like predictive annotation (Prager et al, 2000). The robustness enhancing techniques that were developed for LogAnswer are described in (Glöckner and Pelzer, 2008).…”

Section: The Loganswer Systemmentioning

confidence: 99%

LogAnswer IN QUESTION ANSWERING FORUMS

Pelzer

Glöckner

Dong

2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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Abstract:LogAnswer is a question answering (QA) system for the German language. By providing concise answers to questions of the user, LogAnswer provides more natural access to document collections than conventional search engines do. QA forums provide online venues where human users can ask each other questions and give answers. We describe an ongoing adaptation of LogAnswer to QA forums, aiming at creating a virtual forum user who can respond intelligently and efficiently to human questions. This serves not only as a more accurate evaluation method of our system, but also as a real world use case for automated QA. The basic idea is that the QA system can disburden the human experts from answering routine questions, e.g. questions with known answer in the forum, or questions that can be answered from the Wikipedia. As a result, the users can focus on those questions that really demand human judgement or expertise. In order not to spam users, the QA system needs a good self-assessment of its answer quality. Existing QA techniques, however, are not sufficiently precision-oriented. The need to provide justified answers thus fosters research into logic-oriented QA and novel methods for answer validation.

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Question Answering By Predictive Annotation

Cited by 56 publications

References 0 publications

Passage Reranking for Question Answering Using Syntactic Structures and Answer Types

Passage Reranking for Question Answering Using Syntactic Structures and Answer Types

Towards Identifying the Resolvability of Threads in MOOCs

LogAnswer IN QUESTION ANSWERING FORUMS

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