Topic Modeling the Hàn diăn Ancient Classics (1702.00860v1)

Abstract: Ancient Chinese texts present an area of enormous challenge and opportunity for humanities scholars interested in exploiting computational methods to assist in the development of new insights and interpretations of culturally significant materials. In this paper we describe a collaborative effort between Indiana University and Xi'an Jiaotong University to support exploration and interpretation of a digital corpus of over 18,000 ancient Chinese documents, which we refer to as the "Handian" ancient classics corpus (H`an di\u{a}n g\u{u} j\'i, i.e, the "Han canon" or "Chinese classics"). It contains classics of ancient Chinese philosophy, documents of historical and biographical significance, and literary works. We begin by describing the Digital Humanities context of this joint project, and the advances in humanities computing that made this project feasible. We describe the corpus and introduce our application of probabilistic topic modeling to this corpus, with attention to the particular challenges posed by modeling ancient Chinese documents. We give a specific example of how the software we have developed can be used to aid discovery and interpretation of themes in the corpus. We outline more advanced forms of computer-aided interpretation that are also made possible by the programming interface provided by our system, and the general implications of these methods for understanding the nature of meaning in these texts.

• The paper presents LDA topic modeling to extract latent themes from a corpus of over 18,000 ancient Chinese texts.
• It overcomes challenges of ancient Chinese polysemy by using custom word segmentation and a curated historical dictionary.
• The study demonstrates the integration of digital tools like the InPhO Topic Explorer for interactive analysis of philosophical and historical patterns.

Topic Modeling of the Han dian Ancient Classics

The paper "Topic Modeling the Han dian Ancient Classics (汉典古籍)" presents a collaborative endeavor between Indiana University and Xi'an Jiaotong University. This research explores the application of computational techniques, specifically probabilistic topic modeling using Latent Dirichlet Allocation (LDA), to a robust corpus of over 18,000 ancient Chinese texts known as the "Handian" ancient classics. These texts comprise a diverse array of philosophically, historically, and literarily significant documents central to Chinese cultural heritage.

Methodological Approach

The core contribution of this research is the application of advanced digital humanities techniques to the analysis and interpretation of the Handian corpus. LDA topic modeling is employed to uncover latent thematic structures within the corpus, adopting a "bag of words" approach where texts are analyzed for word frequency distributions irrespective of syntactic order. The paper trained multiple models with varying topic numbers (20, 40, 60, 80, and 100) to strike a balance between interpretative granularity and comprehensiveness. Such methodological choices facilitate the exploration of cultural patterns and intellectual contexts which have historically informed Chinese philosophical traditions.

Technical Challenges and Solutions

A significant challenge addressed by the authors is the language's inherent contextual dependency, particularly in ancient Chinese texts where polysemy and homophony are prevalent. To counteract these issues, a word segmentation process was implemented, utilizing a specially curated dictionary from historical language resources. This approach enhanced the interpretability of models by enabling more nuanced recognition of multi-character words often foundational in classical texts.

Practical Applications

The paper highlights the utilization of the InPhO Topic Explorer, a tool developed in collaboration with Indiana University, to aid in the visualization and thematic exploration of the corpus. The interface, featuring a Hypershelf and interactive topic maps, supports both "distant reading" and "close reading" methodologies. An illustrative case paper involved examining texts related to the "yinyang" concept, demonstrating the capability of the tool to reveal interconnected themes across philosophical and medical domains within the corpus.

Scholarly Implications

This research contributes to the digital humanities by providing an accessible platform for scholars to engage with classical Chinese texts through a computational lens. It foregrounds interpretative issues inherent in topic modeling, emphasizing the need for user-driven exploration in the humanities. The implications extend to discussions about the nature of meaning in texts, suggesting that computational models may offer new perspectives for theoretical debates in philosophy and language studies.

Future Directions

The paper anticipates future work focusing on improved corpus curation, exploring historical and geographical shifts in thematic content, and analyzing authorial behaviors. Additionally, there is an open invitation for traditional curators of scholarly editions to integrate computational methods to further amplify the accessibility and richness of these cultural archives.

The research presented herein underscores the potential of computational modeling to augment traditional humanities scholarship, advocating for a synergistic relationship between humanistic inquiry and technological advancement. This collaboration between Western and Eastern academic institutions exemplifies the promising directions that digital humanities can take in the cross-cultural paper and interpretation of historical texts.