A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures

• URL: http://arxiv.org/abs/2006.11056v1
• Date: Fri, 19 Jun 2020 10:21:17 GMT
• Title: A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
• Authors: Meishan Zhang
• Abstract summary: We focus on two of the most popular formalizations of parsing: constituent parsing and dependency parsing. This article briefly reviews the representative models of constituent parsing and dependency parsing, and also dependency parsing with rich semantics.
• Score: 14.714725860010724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Syntactic and semantic parsing has been investigated for decades, which is one primary topic in the natural language processing community. This article aims for a brief survey on this topic. The parsing community includes many tasks, which are difficult to be covered fully. Here we focus on two of the most popular formalizations of parsing: constituent parsing and dependency parsing. Constituent parsing is majorly targeted to syntactic analysis, and dependency parsing can handle both syntactic and semantic analysis. This article briefly reviews the representative models of constituent parsing and dependency parsing, and also dependency graph parsing with rich semantics. Besides, we also review the closely-related topics such as cross-domain, cross-lingual and joint parsing models, parser application as well as corpus development of parsing in the article.

Related papers

• Integrating Supertag Features into Neural Discontinuous Constituent Parsing [0.0]
  Traditional views of constituency demand that constituents consist of adjacent words, common in languages like German. Transition-based parsing produces trees given raw text input using supervised learning on large annotated corpora.
  arXiv  Detail & Related papers  (2024-10-11T12:28:26Z)
• Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective [0.0]
  We use dependency parsing to analyze news articles in Urdu. We achieve a best-labeled accuracy (LA) of 70% and an unlabeled attachment score (UAS) of 84%.
  arXiv  Detail & Related papers  (2024-06-13T19:30:32Z)
• Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization [2.9950872478176627]
  We study how the presence of a prior sentence can resolve relative clause ambiguities. Results show that a neurosymbolic, based on proof nets, is more open to data bias correction than an approach based on universal dependencies.
  arXiv  Detail & Related papers  (2023-05-24T09:04:18Z)
• Sparse Fuzzy Attention for Structured Sentiment Analysis [48.69930912510414]
  We propose a sparse and fuzzy attention scorer with pooling layers which improves performance and sets the new state-of-the-art on structured sentiment analysis. We further explore the parsing modeling on structured sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance.
  arXiv  Detail & Related papers  (2021-09-14T14:37:56Z)
• Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration [8.25332300240617]
  We show how the concept of nucleus can be defined in the framework of Universal Dependencies. Experiments on 12 languages show that nucleus composition gives small but significant improvements in parsing accuracy.
  arXiv  Detail & Related papers  (2021-01-28T12:22:30Z)
• Context Dependent Semantic Parsing: A Survey [56.69006903481575]
  semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, most semantic parsing methods are not able to utilize contextual information. To address this issue, context dependent semantic parsing has recently drawn a lot of attention.
  arXiv  Detail & Related papers  (2020-11-02T07:51:05Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• A Survey of Unsupervised Dependency Parsing [62.16714720135358]
  Unsupervised dependency parsing aims to learn a dependency from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated text data.
  arXiv  Detail & Related papers  (2020-10-04T10:51:22Z)
• A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text [68.8204255655161]
  This work introduces a new method to consider subjectivity and general context dependency in text analysis. By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
  arXiv  Detail & Related papers  (2020-05-12T21:26:04Z)
• Hierarchical Human Parsing with Typed Part-Relation Reasoning [179.64978033077222]
  How to model human structures is the central theme in this task. We seek to simultaneously exploit the representational capacity of deep graph networks and the hierarchical human structures.
  arXiv  Detail & Related papers  (2020-03-10T16:45:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.