Automated Phrase Mining from Massive Text Corpora (1702.04457v2)

Abstract: As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus. Phrase mining is important in various tasks such as information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. Recently, a few data-driven methods have been developed successfully for extraction of phrases from massive domain-specific text. However, none of the state-of-the-art models is fully automated because they require human experts for designing rules or labeling phrases. Since one can easily obtain many quality phrases from public knowledge bases to a scale that is much larger than that produced by human experts, in this paper, we propose a novel framework for automated phrase mining, AutoPhrase, which leverages this large amount of high-quality phrases in an effective way and achieves better performance compared to limited human labeled phrases. In addition, we develop a POS-guided phrasal segmentation model, which incorporates the shallow syntactic information in part-of-speech (POS) tags to further enhance the performance, when a POS tagger is available. Note that, AutoPhrase can support any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, the new method has shown significant improvements in effectiveness on five real-world datasets across different domains and languages.

• The paper introduces robust positive-only distant training that leverages Wikipedia data to drastically reduce manual labeling in phrase extraction.
• The methodology integrates POS-guided phrasal segmentation to accurately determine phrase boundaries and support multilingual capabilities.
• Empirical validation across five datasets demonstrates significant efficiency gains, domain independence, and enhanced language support.

Automated Phrase Mining from Massive Text Corpora

The paper "Automated Phrase Mining from Massive Text Corpora" presents a novel framework for extracting high-quality phrases from large text datasets. The authors address the limitations of current phrase mining methodologies, most of which require complex linguistic tools and extensive human involvement, which impedes their adaptability to diverse domains and languages.

Key Contributions

The paper introduces two principal innovations:

1. Robust Positive-Only Distant Training: This method leverages extensive public knowledge bases like Wikipedia to gather high-quality phrases, which significantly reduces the need for manual labeling. The positive-only training approach is designed to use existing labeled data from general sources rather than domain-specific, which is typically scarce and costly to obtain. The framework employs an ensemble of decision trees to ensure that any noise in the labeling process (e.g., incorrect phrase identification due to domain-specific variations) is mitigated by the independence of the trees' predictions.
2. POS-Guided Phrasal Segmentation: Incorporating part-of-speech (POS) information into the segmentation process allows the model to dynamically determine phrase boundaries with greater accuracy. This approach balances the need for linguistic insights with the requirement for domain-independence, effectively supporting a multi-lingual capability as long as a general-purpose POS tagger and knowledge base are available for that language.

Empirical Validation

The framework's performance was evaluated across five datasets: abstracts from the DBLP database, business reviews, and Wikipedia articles in English, Spanish, and Chinese. The results indicate that the proposed method outperformed existing methods that rely heavily on hand-engineered linguistic tools and manual domain adaptation.

Significant findings include:

• Domain Independence: The new method significantly reduced the necessity for manual interventions, achieving superior adaptability across various domains and genres.
• Language Support: The method demonstrated strong performance across multiple languages (English, Spanish, and Chinese), suggesting its potential for wide applicability given the availability of language-specific POS taggers and knowledge bases.
• Efficiency Gains: Leveraging distant training and POS-guided segmentation allowed the framework to deliver significant improvements in efficiency, including 80-86% memory savings and 8-11 times speedup in processing time compared to existing baseline methods.

Implications and Future Directions

This paper presents a paradigm shift in automated phrase mining, providing a framework that not only reduces the reliance on manual labels but also extends the applicability to a wider range of languages and domains. The implications for information retrieval, text mining, and natural language processing are substantial, especially as the framework supports the incorporation of new languages and the refinement of entity recognition tasks.

Future developments could explore the refinement of identified phrases into specific entities or concepts, expand the framework's language support, and develop methods that generate the initial positive pools in the absence of comprehensive knowledge bases.

In conclusion, this research offers a scalable, adaptable solution for phrase mining that leverages existing linguistic resources while minimizing domain-specific labor. It sets a new standard for efficiency and versatility in text analysis, thereby inviting further exploration into unsupervised or minimally supervised methods in this field.