[Paper Review] Familia: An Open-Source Toolkit for Industrial Topic Modeling

Di Jiang, Zeyu Chen+3 more|arXiv (Cornell University)|Jul 31, 2017

Topic Modeling6 references3 citations

TL;DR

Familia is an open-source toolkit that provides industrial-grade topic modeling utilities for semantic representation and semantic matching, offering pre-trained LDA, SentenceLDA, and TWE models on large-scale corpora. It enables practical application of topic modeling in tasks like document classification, clustering, and personalized recommendation, with empirical results showing significant performance gains when integrating topic features into machine learning pipelines.

ABSTRACT

Familia is an open-source toolkit for pragmatic topic modeling in industry. Familia abstracts the utilities of topic modeling in industry as two paradigms: semantic representation and semantic matching. Efficient implementations of the two paradigms are made publicly available for the first time. Furthermore, we provide off-the-shelf topic models trained on large-scale industrial corpora, including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word Embedding (TWE). We further describe typical applications which are successfully powered by topic modeling, in order to ease the confusions and difficulties of software engineers during topic model selection and utilization.

Motivation & Objective

• To bridge the gap between academic topic modeling research and industrial application by providing practical, deployable tools.
• To address the lack of open-source implementations for non-LDA topic models commonly used in industry.
• To guide software engineers in selecting and applying topic models effectively through real-world industrial case studies.
• To provide efficient, production-ready implementations of semantic representation and semantic matching paradigms.

Proposed method

• Familia implements two core utility paradigms: semantic representation via MCMC-based inference (Gibbs sampling and Metropolis-Hastings) and semantic matching via similarity computation between text pairs.
• It provides off-the-shelf topic models—LDA, SentenceLDA, and TWE—trained on large-scale industrial corpora for immediate deployment.
• For semantic representation, topic distributions are used as features in downstream models like GBDT and K-means clustering.
• For semantic matching, it supports short-long and long-long text similarity using Hellinger Distance and Jensen-Shannon Divergence on topic distributions.
• TWE integrates LDA topics into word embedding training to improve semantics of low-frequency words.
• The toolkit includes auxiliary functions for topic-word querying, nearest-word retrieval, and model inspection to support model interpretability.

Experimental results

Research questions

• RQ1 How can topic modeling be effectively applied in real industrial systems beyond academic benchmarks?
• RQ2 Which topic models (LDA, SentenceLDA, TWE) are most effective for different types of industrial NLP tasks?
• RQ3 How do topic-based features improve performance in document classification and clustering?
• RQ4 What are the most effective similarity metrics (e.g., HD, JSD) for comparing document topic distributions in recommendation systems?
• RQ5 How can topic modeling be integrated into existing machine learning frameworks like SVDFeature for recommendation?

Key findings

• Integrating LDA-derived topic distributions as augmented features improved GBDT-based news classification performance over baseline features alone.
• Topic modeling enabled effective document clustering, with K-means using LDA topic distributions successfully grouping 1,000 news articles into ten meaningful clusters.
• TWE-based keyword extraction outperformed baseline methods by preserving salient semantic content while filtering out stop words.
• Personalized news recommendation using Hellinger Distance between user profiles (derived from reading history) and article topic distributions significantly improved feed relevance.
• Augmenting the SVDFeature framework with JSD-based topic similarity features led to consistent improvements in both Precision and NDCG for personalized fiction recommendation.