scholarly journals AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing

2019 ◽  
Author(s):  
Xiang Zhang ◽  
Shizhu He ◽  
Kang Liu ◽  
Jun Zhao
Keyword(s):  
Semantic Parsing ◽  
Adaptive Decoding

Learning Compact Lexicons for CCG Semantic Parsing

2014 ◽  
Author(s):  
Yoav Artzi ◽  
Dipanjan Das ◽  
Slav Petrov
Keyword(s):  
Semantic Parsing

Semantic parsing of speech using grammars learned with weak supervision

2015 ◽  
Author(s):  
Judith Gaspers ◽  
Philipp Cimiano ◽  
Britta Wrede
Keyword(s):  
Semantic Parsing ◽  
Weak Supervision

Hierarchical Human Semantic Parsing with Comprehensive Part-Relation Modeling

2021 ◽  
pp. 1-1
Author(s):  
Wenguan Wang ◽  
Tianfei Zhou ◽  
Siyuan Qi ◽  
Jianbing Shen ◽  
Song-Chun Zhu
Keyword(s):  
Semantic Parsing

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 A Cascaded Unsupervised Model for PoS Tagging

2021 ◽  
Vol 20 (1) ◽  
pp. 1-23
Author(s):  
Necva Bölücü ◽  
Burcu Can
Keyword(s):  
Linear Model ◽  
Language Processing ◽  
Bayesian Model ◽  
Linear Models ◽  
Syntactic Category ◽  
Semantic Parsing ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Sentence Level ◽  
Log Linear

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

scholarly journals Active schemata and their role in semantic parsing

1980 ◽  
Author(s):  
Joachim H. Laubsch ◽  
Dietmar F. Roesner
Keyword(s):  
Semantic Parsing
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