Syntactic Topic Models (1002.4665v1)

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.

• The paper introduces a novel Syntactic Topic Model that integrates document-level thematic context with local syntactic roles for unified language modeling.
• It employs a Bayesian non-parametric framework with variational inference to achieve faster convergence and lower perplexity compared to traditional models.
• Experimental results show that the STM effectively segregates functional and content word categories, underscoring its potential for advanced text analysis.

Syntactic Topic Models: Integrating Semantics and Syntax in a Bayesian Framework

The paper "Syntactic Topic Models" by Jordan Boyd-Graber and David M. Blei introduces an advanced probabilistic model termed the Syntactic Topic Model (STM), which aims to integrate syntactic and thematic regularities in LLMing. Utilizing a Bayesian non-parametric approach, the STM aligns semantically coherent word distributions—known as topics—with syntactic coherence in parsed text, thus presenting a holistic model for language understanding.

Overview and Contributions

The STM is a significant extension to traditional topic models, which typically consider semantics while neglecting syntactic structures, and syntactic models, which overlook document-level themes. It posits that each word in a document is influenced by both its document-level thematic context and its local syntactic role within a sentence, as determined by the dependency parse. The main components of the STM include document-level distributions over topics, capturing thematic consistency, and syntactic tree-level distributions, which ensure syntactic coherence.

The innovation of STM lies in its dual focus. Topics are drawn from a convolution, or combination, of both document-level and syntactic context probabilities, which allows the STM to capture the mutual constraints exerted by syntax and semantics on word distributions. This holistic approach offers a more comprehensive view of language than models addressing either syntactic form or semantic content in isolation.

Technical Approach

Posterior inference within the STM is achieved through a variational method, a common strategy in Bayesian models that offers solutions particularly advantageous in scenarios where traditional Markov Chain Monte Carlo (MCMC) approaches may falter. Variational inference allows for approximations over the latent syntactic-semantic structure, characterized by a faster convergence and handling of non-conjugate relationships between model parameters.

The hierarchical Bayesian model underlying the STM dynamically determines the number of thematic-syntactic components, facilitated by a Dirichlet process prior that affords flexibility in modeling and can adapt to new data by extending the latent topic space.

Experimental Evaluation

Through rigorous qualitative and quantitative analysis, STM was compared with existing models like Latent Dirichlet Allocation (LDA) and syntax-centric models across synthetic and real datasets. Key observations included STM's superior ability to predict both functional categories (e.g., prepositions) and content categories (e.g., nouns, verbs), as well as its impressive perplexity reduction compared across different parts of speech.

In synthetic data experiments, the STM efficiently segregated parts of speech while also recognizing thematic variation among nouns, verbs, and adjectives. Document-level thematic structuring, interfaced with syntax-level functional dependency, allowed STM to outperform models that treat words and syntactical structures separately.

Implications and Future Directions

The STM represents an evolution in topic modeling, key for applications requiring nuanced text analysis, such as sentiment analyses or author-specific thematic studies. Its Bayesian non-parametric foundation permits flexible adaptation, making it robust for diverse linguistic datasets.

Beyond the current implementation, potential extensions involve leveraging richer syntactic models that analyze both tree structures independently or incorporating labeled syntactic relations. Such extensions could catalyze advancements in domain adaptation and cross-corpus LLMs, enhancing STM's utility in various AI-driven linguistic applications.

The STM exemplifies the unification of content and structural insights, driving forward probabilistic LLMs in computational linguistics and underscoring the intricate interaction between syntax and semantics in human language understanding.