scholarly journals Semantic Parsing of Ambiguous Input through Paraphrasing and Verification

2015 ◽  
Vol 3 ◽  
pp. 571-584
Author(s):  
Philip Arthur ◽  
Graham Neubig ◽  
Sakriani Sakti ◽  
Tomoki Toda ◽  
Satoshi Nakamura
Keyword(s):  
Natural Language ◽  
Language Model ◽  
Input String ◽  
New Method ◽  
Semantic Parsing ◽  
Search Queries ◽  
Input Sentence ◽  
Meaning Representation ◽  
Context Free ◽  
Do So

We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can be used to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.

scholarly journals Splittability of Bilexical Context-Free Grammars is Undecidable

2011 ◽  
Vol 37 (4) ◽  
pp. 867-879
Author(s):  
Mark-Jan Nederhof ◽  
Giorgio Satta
Keyword(s):  
Dynamic Programming ◽  
Natural Language ◽  
Input String ◽  
Running Time ◽  
Natural Language Parsing ◽  
Central Interest ◽  
The Right ◽  
Programming Algorithms ◽  
Context Free ◽  
Context Free Grammars

Bilexical context-free grammars (2-LCFGs) have proved to be accurate models for statistical natural language parsing. Existing dynamic programming algorithms used to parse sentences under these models have running time of O(∣w∣4), where w is the input string. A 2-LCFG is splittable if the left arguments of a lexical head are always independent of the right arguments, and vice versa. When a 2-LCFGs is splittable, parsing time can be asymptotically improved to O(∣w∣3). Testing this property is therefore of central interest to parsing efficiency. In this article, however, we show the negative result that splittability of 2-LCFGs is undecidable.

scholarly journals A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing

2014 ◽  
Vol 2 ◽  
pp. 547-560 ◽  
Author(s):  
Andreas Vlachos ◽  
Stephen Clark
Keyword(s):  
Information System ◽  
Natural Language ◽  
Learning Algorithm ◽  
Imitation Learning ◽  
Semantic Parsing ◽  
Learning Framework ◽  
Test Sets ◽  
Context Dependent ◽  
Meaning Representation ◽  
Tourist Information

Semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora which assume that all utterances must be interpreted according to a database and typically ignore context. In this paper we present a new, publicly available corpus for context-dependent semantic parsing. The MRL used for the annotation was designed to support a portable, interactive tourist information system. We develop a semantic parser for this corpus by adapting the imitation learning algorithm DAgger without requiring alignment information during training. DAgger improves upon independently trained classifiers by 9.0 and 4.8 points in F-score on the development and test sets respectively.

scholarly journals Learning to Map Frequent Phrases to Sub-Structures of Meaning Representation for Neural Semantic Parsing

2020 ◽  
Vol 34 (05) ◽  
pp. 7546-7553
Author(s):  
Bo Chen ◽  
Xianpei Han ◽  
Ben He ◽  
Le Sun
Keyword(s):  
Natural Language ◽  
Performance Improvement ◽  
Search Space ◽  
Great Benefit ◽  
Semantic Parsing ◽  
Mismatch Problem ◽  
Weakly Supervised ◽  
Meaning Representation

Neural semantic parsers usually generate meaning representation tokens from natural language tokens via an encoder-decoder model. However, there is often a vocabulary-mismatch problem between natural language utterances and logical forms. That is, one word maps to several atomic logical tokens, which need to be handled as a whole, rather than individual logical tokens at multiple steps. In this paper, we propose that the vocabulary-mismatch problem can be effectively resolved by leveraging appropriate logical tokens. Specifically, we exploit macro actions, which are of the same granularity of words/phrases, and allow the model to learn mappings from frequent phrases to corresponding sub-structures of meaning representation. Furthermore, macro actions are compact, and therefore utilizing them can significantly reduce the search space, which brings a great benefit to weakly supervised semantic parsing. Experiments show that our method leads to substantial performance improvement on three benchmarks, in both supervised and weakly supervised settings.

scholarly journals Break It Down: A Question Understanding Benchmark

2020 ◽  
Vol 8 ◽  
pp. 183-198
Author(s):  
Tomer Wolfson ◽  
Mor Geva ◽  
Ankit Gupta ◽  
Matt Gardner ◽  
Yoav Goldberg ◽  
...  
Keyword(s):  
Natural Language ◽  
Formal Language ◽  
Question Answering ◽  
Semantic Parsing ◽  
Open Domain ◽  
Meaning Representation

Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.

scholarly journals Winograd Schemas in Portuguese

2019 ◽  
Author(s):  
Gabriela Melo ◽  
Vinicius Imaizumi ◽  
Fábio Cozman
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Language Model ◽  
Do So

The Winograd Schema Challenge has become a common benchmark for question answering and natural language processing. The original set of Winograd Schemas was created in English; in order to stimulate the development of Natural Language Processing in Portuguese, we have developed a set of Winograd Schemas in Portuguese. We have also adapted solutions proposed for the English-based version of the challenge so as to have an initial baseline for its Portuguese-based version; to do so, we created a language model for Portuguese based on a set of Wikipedia documents.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

scholarly journals On the accuracy of different neural language model approaches to ADE extraction in natural language corpora

2021 ◽  
Vol 190 ◽  
pp. 706-711
Author(s):  
Alexander Sboev ◽  
Anton Selivanov ◽  
Gleb Rylkov ◽  
Roman Rybka
Keyword(s):  
Natural Language ◽  
Language Model

Parsing

2017 ◽  
Author(s):  
John Carroll
Keyword(s):  
Natural Language ◽  
Dependency Parsing ◽  
Grammatical Structure ◽  
Linguistic Information ◽  
Natural Language Parsing ◽  
Feature Structures ◽  
Key Concepts ◽  
Learning From Text ◽  
Dependency Structures ◽  
Context Free

This chapter introduces key concepts and techniques for natural-language parsing: that is, finding the grammatical structure of sentences. The chapter introduces the fundamental algorithms for parsing with context-free (CF) phrase structure grammars, how these deal with ambiguous grammars, and how CF grammars and associated disambiguation models can be derived from syntactically annotated text. It goes on to consider dependency analysis, and outlines the main approaches to dependency parsing based both on manually written grammars and on learning from text annotated with dependency structures. It finishes with an overview of techniques used for parsing with grammars that use feature structures to encode linguistic information.

scholarly journals EMOSIS Sentiment Analysis on Tweets with Emotion and Intensity Level Recognition Considering Ending Punctuation Marks

2019 ◽  
Vol 8 (4) ◽  
pp. 10289-10293
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Significant Role ◽  
Language Model ◽  
Intensity Level ◽  
Processing Stage ◽  
Overall Performance

Sentiment Analysis is a tool used for determining the Polarity or Emotion of a Sentence. It is a field of Natural Language Processing which focuses on the study of opinions. In this study, the researchers solved one key challenge in Sentiment Analysis, which is to consider the Ending Punctuation Marks present in a sentence. Ending punctuation marks plays a significant role in Emotion Recognition and Intensity Level Recognition. The research made used of tweets expressing opinions about Philippine President Rodrigo Duterte. These downloaded tweets served as the inputs. It was initially subjected to pre-processing stage to be able to prepare the sentences for processing. A Language Model was created to serve as the classifier for determining the scores of the tweets. The scores give the polarity of the sentence. Accuracy is very important in sentiment analysis. To increase the chance of correctly identifying the polarity of the tweets, the input undergone Intensity Level Recognition which determines the intensifiers and negations within the sentences. The system was evaluated with overall performance of 80.27%.

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