Open Domain Question Answering via Semantic Enrichment

By Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, Ming-Wei Chang. 2015

116 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Reading Wikipedia to Answer Open-Domain Questions (2017, methodology): “There are a number of highly developed full pipeline QA approaches using either the Web, as does QuASE (Sun et al., 2015), or Wikipedia as a resource, as do Microsoft’s AskMSR (Brill et al., 2002), IBM’s DeepQA (Ferrucci et al., 2010) and YodaQA (Baudi ˇ s, 2015; Baudi ˇ s and ˇSediv ` y, 2015) — th”
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases (2016, methodology): “y X produce?". Once extracted, such structured information is used in many ways, e.g., as primitives in information extraction, knowledge base population [10, 52], and question-answering systems [48, 3]. Traditional systems for relation extraction [2, 9, 17] partition the process into several s”
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks (2017, background): “, 2015; Savenkov and Agichtein, 2016) or improves the inference on text using KB (Sun et al., 2015).”
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering (2021, background): “human champions on the American TV show “Jeopardy”; QuASE [118] searches for a list of most prominent sentences from a Web search engine (e.”
Enabling deep learning for large scale question answering in Italian (2019, unknown): “Some of these rely on structured resources, such as Freebase, while others use unstructured information from sources such as Wikipedia (an example of such a system is the Microsoft’s AskMSR [3]), or generic Web pages, e.g. the QuASE system [16].”
Question Answering on Freebase via Relation Extraction and Textual Evidence (2016, background): “Our work also has similarities to Sun et al. (2015) who does question answering on unstructured data but enrich it with Freebase, a reversal of our pipeline.”
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text (2016, background): “For example, large-scale knowledge bases (KB), such as Freebase (Bollacker et al., 2008) or DBPedia (Auer et al., 2007), have proven to be important resources for supporting open-domain question answering (Berant et al., 2013; Sun et al., 2015; Yih et al., 2015).”