A survey on question answering technology from an information retrieval perspective

By Oleksandr Kolomiyets, Marie-Francine Moens. 2011

279 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Retrieval & Search, Question Answering & Reading Comprehension

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Citations in the corpus

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems (2015, background): “Note that while (non-learning) IR systems can also be applied to other tasks such as QA (Kolomiyets & Moens, 2011) they require signiﬁcant tuning to do so.”
Question Answering with Subgraph Embeddings (2014, background): “Information retrieval systems first retrieve a broad set of candidate answers by querying the search API of KBs with a transformation of the question into a valid query and then use fine-grained detection heuristics to identify the exact answer (Kolomiyets and Moens, 2011; Unger et al., 2012; Yao an”
Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “ailable. The advances in structured KBs, have driven an increasing interest in the NLP and AI communities in the problem of natural language question answering using structured KBs (refer to as KBQA) [4, 7, 8, 16, 23, 25, 41, 26, 38]. The VQA approach which is closest to KB-QA (and to our approach) ”
FVQA: Fact-Based Visual Question Answering (2016, background): “KB-QA approaches can be divided into two categories: semantic parsing methods [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47] that try to translate questions into accurate logical expressions and then map to KB queries, and information retrieval methods [48], [49], [50], [51], [52],”
Survey on evaluation methods for dialogue systems (2019, background): “There is a large amount of research in the area of single-turn QA and there are several surveys, we refer the reader to: Kolomiyets and Moens (2011); Diefenbach et al.”
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering (2018, background): “There has been interest in the natural language processing community in answering questions based on knowledge bases (KBs) using either semantic parsing [37,38,39,40,41,42,43,44,45,46,47] or information retrieval [48,49,50,51,52,53,54] methods.”
Evaluating Natural Language Understanding Services for Conversational Question Answering Systems (2017, background): “A comprehensive and extensive survey of question answering technologies was presented by Kolomiyets and Moens (2011).”
Visual question answering: A survey of methods and datasets (2016, background): “On the one hand, information retrieval approaches use unstructured collections of documents, in which the key words of the question are looked for to identify relevant passages and sentences [33, 40].”
Core techniques of question answering systems over knowledge bases: a survey (2017, unknown): “There have been various surveys onQASystems in the field of IR driven by the Text Retrieval Conference (TREC), theCross-LanguageEvaluation Forum (CLEF) andNII Test Collections for IR systems (NTCIR) campaigns, see [3,29,49].”
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering (2021, background): “In literature, OpenQA has been studied closely with research in Natural Language Processing (NLP), Information Retrieval (IR), and Information Extraction (IE) [7], [8], [9],”