Mohamed Yahya


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ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
Abdalghani Abujabal | Rishiraj Saha Roy | Mohamed Yahya | Gerhard Weikum
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as compositionality, temporal reasoning, and comparisons. ComQA questions come from the WikiAnswers community QA platform, which typically contains questions that are not satisfactorily answerable by existing search engine technology. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.


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QUINT: Interpretable Question Answering over Knowledge Bases
Abdalghani Abujabal | Rishiraj Saha Roy | Mohamed Yahya | Gerhard Weikum
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present QUINT, a live system for question answering over knowledge bases. QUINT automatically learns role-aligned utterance-query templates from user questions paired with their answers. When QUINT answers a question, it visualizes the complete derivation sequence from the natural language utterance to the final answer. The derivation provides an explanation of how the syntactic structure of the question was used to derive the structure of a SPARQL query, and how the phrases in the question were used to instantiate different parts of the query. When an answer seems unsatisfactory, the derivation provides valuable insights towards reformulating the question.


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ReNoun: Fact Extraction for Nominal Attributes
Mohamed Yahya | Steven Whang | Rahul Gupta | Alon Halevy
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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Natural Language Questions for the Web of Data
Mohamed Yahya | Klaus Berberich | Shady Elbassuoni | Maya Ramanath | Volker Tresp | Gerhard Weikum
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning