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Besides semantic parsing for querying databases, previous work has looked at interpreting natural language for performing programming tasks, playing computer games, following navigational instructions, and interacting in the real world via perception

Semantic Parsing on Freebase from Question-Answer Pairs.

EMNLP, pp.1533-1544, (2013)

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Abstract

Occidental College, Columbia University In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main challenge in this setting is narrowing down the huge number of possible logical p...More

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Introduction
  • The authors focus on the problem of semantic parsing natural language utterances into logical forms that can be executed to produce denotations.
  • The goal of this paper is to do both: learn a semantic parser without annotated logical forms that scales to the large number of predicates on Freebase.
  • While limited-domain semantic parsers are able to learn the lexicon from per-example supervision (Kwiatkowski et al, 2011; Liang et al, 2011), at large scale they have inadequate coverage (Cai and Yates, 2013).
  • Previous work on semantic parsing on Freebase uses a combination of manual rules (Yahya et al, 2012; Unger et al, 2012), distant supervision (Krishnamurthy and Mitchell, 2012), and schema
Highlights
Methods
  • The authors evaluate the semantic parser empirically.
  • In Section 4.1, the authors compare the approach to Cai and Yates (2013) on their recently released dataset and present results on a new dataset that the authors collected.
  • In Section 4.2, the authors provide detailed experiments to provide additional insight on the system.
  • Setup The authors implemented a standard beam-based bottom-up parser which stores the k-best derivations for each span.
  • The authors use k = 500 for all the experiments on FREE917 and k = 200 on WEBQUESTIONS.
  • In experiments on WEBQUESTIONS, D (x) contained 197 derivations on average
Results
  • AMT workers sometimes provide partial answers, e.g., the answer to “What movies does Taylor Lautner play in?” is a set of 17 entities, out of which only 10 appear on the Freebase page.
  • In the question “What kind of system of government does the United States have?” the phrase “United States” maps to 231 entities in the lexicon, the verb “have” maps to 203 binaries, and the phrases “kind”, “system”, and “government” all map to many different unary and binary predicates.
  • Parsing correctly involves skipping some words, mapping other words to predicates, while resolving many ambiguities in the way that the various predicates can combine
Conclusion
Tables
  • Table1: Full set of features. For the alignment and text similarity, r1 is a phrase, r2 is a predicate with Freebase name s2, and b is a binary predicate with type signature (t1, t2)
  • Table2: Three examples of the bridging operation. The bridging binary predicate b is in boldface
  • Table3: Statistics on various semantic parsing datasets. Our new dataset, WEBQUESTIONS, is much larger than FREE917 and much more lexically diverse than ATIS
  • Table4: Accuracies on the development set under different schemes of binary predicate generation. In ALIGNMENT, binaries are generated only via the alignment lexicon. In BRIDGING, binaries are generated through the bridging operation only. ALIGNMENT+BRIDGING corresponds to the full system
  • Table5: Accuracies on the development set with features removed. POS and DENOTATION refer to the POS tag and denotation features from Section 3.3
  • Table6: Accuracies on the development set using either unlexicalized alignment features (ALIGNMENT) or lexicalized features (LEXICALIZED)
Download tables as Excel
Funding
  • The authors gratefully acknowledge the support of the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) prime contract no
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