Large-scale Semantic Parsing via Schema Matching and Lexicon Extension

By Qingqing Cai, Alexander Yates. 2013

302 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension (2017, background): “Proposed datasets (Cai and Yates, 2013; Berant et al., 2013; Bordes et al., 2015) are either limited in scale or in the complexity of questions, and can only retrieve facts covered by the KB.”
Semantic Parsing on Freebase from Question-Answer Pairs (2013, methodology): “, 2011), at large scale they have inadequate coverage (Cai and Yates, 2013).”
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017, methodology): “The datasets are GeoQuery880 (Tang & Mooney, 2001), ATIS (Price, 1990), Free917 (Cai & Yates, 2013), Overnight (Wang et al., 2015), WebQues-tions (Berant et al., 2013), and WikiTableQues-tions (Pasupat & Liang, 2015).”
Learning to Automatically Solve Algebra Word Problems (2014, background): “Examples include question answering (Clarke et al., 2010; Cai and Yates, 2013a; Cai and Yates, 2013b; Berant et al., 2013; Kwiatkowski et al., 2013), dialog systems (Artzi and Zettlemoyer, 2011), robot instruction (Chen and Mooney, 2011; Chen, 2012; Kim and Mooney, 2012; Matuszek et al.”
Semantic Parsing via Paraphrasing (2014, methodology): “We compare our system to Cai and Yates (2013) (CY13), Berant et al. (2013) (BCFL13), and Kwiatkowski et al. (2013) (KCAZ13).”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, methodology): “We report performance on two benchmark datasets: GeoQuery (Zelle and Mooney, 1996) and Freebase QA (FQ) (Cai and Yates, 2013a).”
Building a Semantic Parser Overnight (2015, background): “Much of current excitement around semantic parsing emphasizes large knowledge bases such as Freebase (Cai and Yates, 2013; Kwiatkowski et al., 2013; Berant et al., 2013).”
Open Question Answering with Weakly Supervised Embedding Models (2014, background): “Recently, new systems for learning question answering systems with few labeled data have been introduced based on semantic parsers [6,3,12].”
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) ”
Semantic Parsing for Single-Relation Question Answering (2014, background): “Very recently, researchers have started developing semantic parsers for large, generaldomain knowledge bases like Freebase and DBpedia (Cai and Yates, 2013; Berant et al., 2013; Kwiatkowski et al., 2013).”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “Another type of semantic parsing work (Zelle & Mooney, 1996; Zettlemoyer & Collins, 2005; Zettlemoyer & Collins, 2007; Wong & Mooney, 2007; Cai & Yates, 2013; Berant et al., 2013; Kwiatkowski et al., 2013; Berant & Liang, 2014) maps NL text into logical forms by supervised or semi-supervised…”
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base (2015, methodology): “Several semantic parsing methods use a domainindependent meaning representation derived from the combinatory categorial grammar (CCG) parses (e.g., (Cai and Yates, 2013; Kwiatkowski et al., 2013; Reddy et al., 2014)).”
Information Extraction over Structured Data: Question Answering with Freebase (2014, methodology): “To alleviate this problem, the best attempt so far is to map from ReVerb (Fader et al., 2011) predicateargument triples to Freebase relation triples (Cai and Yates, 2013; Berant et al., 2013).”
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],”
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables (2017, background): “More recent data sets for question answering against Freebase have a much broader domain, but simple questions (Berant et al., 2013; Cai and Yates, 2013).”