Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing

By Lappoon R. Tang, Raymond J. Mooney. 2001

281 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, background): “…(as opposed to spoken) natural language queries against a database (Zelle and Mooney, 1996) and it also continues to spur research (see for example Goldwasser et al., 2011), as does the similar Jobs database, which provides mappings of 640 sentences to a listing of jobs (Tang and Mooney, 2001).”
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars (2005, methodology): “We compare the structured classiﬁer results to the C OCK - TAIL system (Tang & Mooney, 2001).”
Learning Dependency-Based Compositional Semantics (2011, background): “In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. law.”
Measuring Coding Challenge Competence With APPS (2021, background): “Zelle and Mooney (1996) and Tang and Mooney (2001) precedes Yu et al. (2018) by also facilitating the synthesis of database queries, though more recent program synthesis works such as Wang et al. (2019c) use Spider from Yu et al. (2018).”
Learning for Semantic Parsing with Statistical Machine Translation (2006, methodology): “, 2005), COCKTAIL (Tang and Mooney, 2001), SCISSOR (Ge and Mooney, 2005) and Zettlemoyer and Collins (2005).”
Semantic Parsing for Single-Relation Question Answering (2014, methodology): “Research in this line originally used small, domain-speciﬁc databases, such as GeoQuery (Tang and Mooney, 2001; Liang et al., 2013).”
Information Extraction over Structured Data: Question Answering with Freebase (2014, background): “Researchers in semantic parsing have recently explored QA over Freebase as a way of moving beyond closed domains such as GeoQuery (Tang and Mooney, 2001).”
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System (2018, background): “user written expert written given NL (Tang and Mooney, 2001) GEO880 DSL 880 284 60 7.”
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2020, methodology): “More broadly, compression-based criteria have been successfully used by a variety of models for language (Grünwald, 1995; Tang and Mooney, 2001; Ravi and Knight, 2009; Poon et al., 2009).”
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (2018, background): “…my_list in descending order z Figure 1: Graphical Representation of STRUCTVAE While these models have a long history (Zelle and Mooney, 1996; Tang and Mooney, 2001), recent advances are largely attributed to the success of neural network models (Xiao et al., 2016; Ling et al., 2016; Dong…”
Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer (2004, methodology): “Mooney’s research group has been developing successful methods for learning to translate NL into formal semantic representations (Zelle & Mooney 1996; Thompson & Mooney 2003; Tang & Mooney 2001).”
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases (2019, unknown): “Early approaches depended on grammars that were manually-speciﬁed [4] or learned from database-speciﬁc training examples [14], [35], making it difﬁcult to scale them across different database schemas.”
Driving Semantic Parsing from the World’s Response (2010, methodology): “We evaluate our learning approach and model on the well studied Geoquery domain (Zelle and Mooney, 1996; Tang and Mooney, 2001), a database consisting of U.”
Towards a theory of natural language interfaces to databases (2003, methodology): “First, semantically tractable questions are in fact quite prevalent (from 77.5% to 97% of the questions in Mooney s data).”
Smatch: an Evaluation Metric for Semantic Feature Structures (2013, unknown): “Current whole-sentence semantic parsing is mainly evaluated in two ways: 1. task correctness (Tang and Mooney, 2001), which evaluates on an NLP task that uses the parsing re-sults; 2. whole-sentence accuracy (Zettlemoyer and Collins, 2005), which counts the number of sentences parsed completely…”
Grounding language acquisition by training semantic parsers using captioned videos (2018, methodology): “This is comparable to the size of other datasets used for semantic parsing such as two datasets from Tang and Mooney (2001) with 880 and 640 examples respectively and the navigation instruction dataset (Chen and Mooney, 2011) with 706 examples (containing 3236 single sentences).”