Scalable Semantic Parsing with Partial Ontologies

By Eunsol Choi, Tom Kwiatkowski, Luke S. Zettlemoyer. 2015

36 citations recorded by the source corpus.

We consider the problem of building scalable semantic parsers for Freebase, and present a new approach for learning to do partial analyses that ground as much of the input text as possible without requiring that all content words be mapped to Freebase concepts. We study this problem on two newly introduced large-scale noun phrase datasets, and present a new semantic parsing model and semi-supervised learning approach for reasoning with partial ontological support. Experiments demonstrate strong performance on two tasks: referring expression resolution and entity attribute extraction. In both cases, the partial analyses allow us to improve precision over strong baselines, while parsing many phrases that would be ignored by existing techniques.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Break It Down: A Question Understanding Benchmark (2020, methodology): “Kwiatkowski et al. (2013) and Choi et al. (2015) used underspecified logical forms as an intermediate representation.”
Learning a Neural Semantic Parser from User Feedback (2017, background): “2013), distant supervision (Krishnamurthy and Mitchell, 2012; Choi et al., 2015), and binary correct/incorrect feedback signals (Clarke et al.”
Joint learning of ontology and semantic parser from text (2016, unknown): “In a very similar fashion, [9] deﬁnes underspeciﬁed entities, types and relations, when the corresponding concept does not exist in Freebase.”
Transforming Dependency Structures to Logical Forms for Semantic Parsing (2016, background): “…been studied on their own and as a component in tasks such as semantic parsing to knowledge bases (Kwiatkowski et al., 2013; Reddy et al., 2014; Choi et al., 2015; Krishna-murthy and Mitchell, 2015), sentence simpliﬁcation (Narayan and Gardent, 2014), summarization (Liu et al., 2015),…”
Semantic Parsing to Probabilistic Programs for Situated Question Answering (2016, unknown): “(Chen and Mooney, 2011; Artzi and Zettlemoyer, 2013), and information extraction (Krishnamurthy and Mitchell, 2012; Choi et al., 2015).”
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks (2017, methodology): “ence on the KB using text based features (Krishnamurthy and Mitchell, 2012; Reddy et al., 2014; Joshi et al., 2014; Yao and Van Durme, 2014; Yih et al., 2015; Neelakantan et al., 2015b; Guu et al., 2015; Xu et al., 2016b; Choi et al., 2015; Savenkov and Agichtein, 2016) or improves the inference on ”