Syntactic Topic Models
We develop \name\ (STM), a nonparametric Bayesian model of parsed documents. \Shortname\ generates words that are both thematically and syntactically constrained, which combines the semantic insights of topic models with the syntactic information available from parse trees. Each word of a sentence is generated by a distribution that combines document-specific topic weights and parse-tree specific syntactic transitions. Words are assumed generated in an order that respects the parse tree. We derive an approximate posterior inference method based on variational methods for hierarchical Dirichlet processes, and we report qualitative and quantitative results on both synthetic data and hand-parsed documents.
Code (0)
등록된 구현이 없습니다.
Tasks
SentenceTopic ModelsSimilar Papers 제목 키워드 기반
A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
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 include…
Dependency ParsingSemantic ParsingSyntactic Recurrent Neural Network for Authorship Attribution
Writing style is a combination of consistent decisions at different levels of language production including lexical, syntactic, and structural associated to a specific author (or author groups). While lexical-based model…
Authorship Attributiontext-classificationText ClassificationRethinking Benign Relearning: Syntax as the Hidden Driver of Unlearning Failures
Machine unlearning aims to remove specific content from trained models while preserving overall performance. However, the phenomenon of benign relearning, in which forgotten information reemerges even from benign fine-tu…
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequenti…
Language ModelingLanguage ModellingSentiment AnalysisTopic ModelsExploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been …
Language ModelingLanguage Modelling