Anchor-Free Correlated Topic Modeling.

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Topic modeling is crucial for text analysis, but relies on the fragile anchor-word assumption for topic identifiability.
• Existing methods using higher-order statistics require more data and additional assumptions, limiting practical application.
• The anchor-word assumption, where a word uniquely defines a topic, is often violated in real-world text data.

Purpose of the Study:

• To develop a new topic identification criterion that does not rely on the anchor-word assumption.
• To theoretically guarantee topic identifiability even when the anchor-word assumption is significantly violated.
• To propose efficient algorithms for solving the resulting topic identification problem.

Main Methods:

• A novel topic identification criterion based on second-order word co-occurrence statistics is proposed.
• Two algorithms are presented: alternating optimization for high performance and a primal-dual algorithm for speed.
• The methods are evaluated on diverse real-world text corpora.

Main Results:

• The proposed anchor-free method demonstrates significant improvements over anchor-word based approaches.
• The criterion theoretically guarantees topic identification irrespective of the anchor-word assumption's validity.
• The primal-dual algorithm achieved up to 200x speedup compared to the alternating optimization method.

Conclusions:

• The developed anchor-free topic modeling approach offers a robust and effective solution for text analysis.
• This method overcomes limitations of traditional approaches, providing reliable topic identification in complex datasets.
• The proposed algorithms offer practical and scalable solutions for large-scale text corpora analysis.