Syntactic Topic Models

Abstract

The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents. It assumes that each word is drawn from a latent topic chosen by combining document-level features and the local syntactic context. Each document has a distribution over latent topics, as in topic models, which provides the semantic consistency. Each element in the dependency parse tree also has a distribution over the topics of its children, as in latent-state syntax models, which provides the syntactic consistency. These distributions are convolved so that the topic of each word is likely under both its document and syntactic context. We derive a fast posterior inference algorithm based on variational methods. We report qualitative and quantitative studies on both synthetic data and hand-parsed documents. We show that the STM is a more predictive model of language than current models based only on syntax or only on topics.

Cited by in corpus (11)

• Hierarchical relational models for document networks
• Representation Learning for Natural Language Processing
• Topic Segmentation and Labeling in Asynchronous Conversations
• Top2Vec: Distributed Representations of Topics
• A Tutorial on Deep Latent Variable Models of Natural Language
• Using Variational Inference and MapReduce to Scale Topic Modeling
• Learning Frames from Text with an Unsupervised Latent Variable Model
• On a Topic Model for Sentences
• Word Representations, Tree Models and Syntactic Functions
• Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation
• Bilingual Topic Models for Comparable Corpora