Web question answering: is more always better?

By Susan T. Dumais, Michele Banko, Eric Brill, Jimmy J. Lin, Andrew Y. Ng. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

351 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0c7c2ca0579112c81d5a98fb0d110d1a006759b5

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In Artificial Intelligence

Citations in the corpus

Neural information retrieval: at the end of the early years (2017, background): “First is question answering (QA) and community question answering (CQA) tasks, which perhaps represent the greatest cross-over between the NLP and IR fields (e.g., see Dumais et al. 2002).”
Factoid Question Answering over Unstructured and Structured Web Content (2005, methodology): “…a predefined ontology of answer types, while the latter reduces the number of candidate answers that co-occur frequently with the question words in web search snippets but are not related to the question intent and also eliminates the need of an n-gram assembly stage, as employed in [2] and [5].”
Systematic review automation technologies (2014, methodology): “For this task, a form of an automatic web-based question-answering system [38,39] in which”
Be Consistent! Improving Procedural Text Comprehension using Label Consistency (2019, background): “It has been studied in computer vision (Haeusser et al., 2017; Chen et al., 2018) and IR (Clarke et al., 2001; Dumais et al., 2002).”
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus (2016, background): “The question answering field has focused on building QA systems based on traditional information retrieval procedures (Lopez et al., 2011; Dumais et al., 2002; Voorhees and Tice, 2000).”
Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering (2017, background): “Specifically in the realm of question answering, researchers have long known that, all things being equal, larger collections yield higher effectiveness due to data redundancy [3, 4].”
Introduction to the Special Issue on the Web as Corpus (2003, methodology): “The Web has recently been used by groups at Shefﬁeld and Microsoft, among others, as a source of answers for question-answering applications, in a merge of search engine and language-processing technologies (Greenwood, Roberts, and Gaizauskas 2002; Dumais et al. 2002).”
Beliefs and biases in web search (2013, background): “Technology developed for Web-scale question answering (e.g., [12]) may be applied in the ranking of search results to help search engines consider base rates, especially for particular queries or query classes (e.g., as we demonstrate in our analysis with yes-no questions beginning with “can”).”
Autonomously semantifying wikipedia (2007, background): “Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, neutral): “matching [27, 53, 92].”
Measuring the Feasibility of a Question and Answering System for the Sarawak Gazette Using Chatbot Technology (2025, unknown): “QASes aim to automatically provide the best answers to queries expressed by the user in natural language (Hovy, Gerber, Hermjakob, Junk & Lin, 2000; Dumais et al, 2002; Ong, Day & Hsu, 2009; Ojokoh & Adebisi, 2019).”