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 (Hàn diăn gŭ jí, 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 concept of literary genre is a highly complex one: not only are different genres frequently defined on several, but not necessarily the same levels of description, but consideration of genres as cognitive, social, or scholarly constructs with a rich history further complicate the matter. This contribution focuses on thematic aspects of genre with a quantitative approach, namely Topic Modeling. Topic Modeling has proven to be useful to discover thematic patterns and trends in large collections of texts, with a view to class or browse them on the basis of their dominant themes. It has rarely…
Generating lyrics and poems is one of the essential downstream tasks in the Natural Language Processing (NLP) field. Current methods have performed well in some lyrics generation scenarios but need further improvements in tasks requiring fine control. We propose a novel method for generating ancient Chinese lyrics (Song Ci), a type of ancient lyrics that involves precise control of song structure. The system is equipped with a phrase retriever and a phrase connector. Based on an input prompt, the phrase retriever picks phrases from a database to construct a phrase pool. The phrase connector th…
Topic models are widely used in studying social phenomena. We conduct a comparative study examining state-of-the-art neural versus non-neural topic models, performing a rigorous quantitative and qualitative assessment on a dataset of tweets about the COVID-19 pandemic. Our results show that not only do neural topic models outperform their classical counterparts on standard evaluation metrics, but they also produce more coherent topics, which are of great benefit when studying complex social problems. We also propose a novel regularization term for neural topic models, which is designed to addr…
Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Contemporary neural topic models surpass classical ones according to these metrics. At the same time, topic model evaluation suffers from a validation gap: automated coherence, developed for classical models, has not been validated using human experimentation for neural models. In addition, a meta-analysis of topic modeling literature reveals a substant…
In this paper we present a model for unsupervised topic discovery in texts corpora. The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents. These probabilities are used to recover by marginalization probabilities of words given documents. For very large corpora where the number of documents can be in the order of billions, using a neural auto-encoder based document embedding is more scalable then using a lookup table embedding as classically done. We…
Topic models can be useful tools to discover latent topics in collections of documents. Recent studies have shown the feasibility of approach topic modeling as a clustering task. We present BERTopic, a topic model that extends this process by extracting coherent topic representation through the development of a class-based variation of TF-IDF. More specifically, BERTopic generates document embedding with pre-trained transformer-based language models, clusters these embeddings, and finally, generates topic representations with the class-based TF-IDF procedure. BERTopic generates coherent topics…
ARC Centre of Excellence for Mathematical and Statistical Frontiers（澳大利亚研究理事会数学与统计前沿卓越中心）；The University of Adelaide（阿德莱德大学）；Data to Decisions CRC（数据决策合作研究中心）
In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in traditional statistical techniques, the need arises for frameworks whereby advanced NLP techniques such as topic modelling may be incorporated within classical methodologies. This paper provides a classical, supervised, statistical learning framework for prediction from text, using topic models as a data reduction method and the topics themselves a…
Johns Hopkins University（约翰斯·霍普金斯大学）；Department of Computer Science（计算机科学系）；Baltimore, MD, USA（美国马里兰州巴尔的摩）
Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our appr…
Recently, the relationship between automated and human evaluation of topic models has been called into question. Method developers have staked the efficacy of new topic model variants on automated measures, and their failure to approximate human preferences places these models on uncertain ground. Moreover, existing evaluation paradigms are often divorced from real-world use. Motivated by content analysis as a dominant real-world use case for topic modeling, we analyze two related aspects of topic models that affect their effectiveness and trustworthiness in practice for that purpose: the stab…
LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Science and Technology Beijing（北京科技大学）；Beijing Value Simplex Technology Co. Ltd（北京价值简单科技有限公司）；Zhongguancun Laboratory（中关村实验室）
KAIST（韩国科学技术院）；Sungkyunkwan University（成均馆大学）；New York University（纽约大学）
Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters and not understood by modern Korean or Chinese speakers. Historians with expertise in this time period have been analyzing the documents, but that process is very difficult and time-consuming, and language models would significantly speed up the process. Toward building and evaluating language models for Hanja, we release the Hanja Understanding Evaluation dataset consisting of chronological attribution, topic classification, named entity recognition, and summary…
Fùxì: A Benchmark for Evaluating Language Models on Ancient Chinese Text Understanding and Generation
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Lab of Artificial Intelligence for Education, East China Normal University（华东师范大学教育人工智能实验室）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（华东师范大学上海教育人工智能研究院）
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts
College of Information Management, Nanjing Agricultural University（南京农业大学信息管理学院）；School of Economics & Management, Nanjing University of Science and Technology（南京理工大学经济管理学院）；College of Liberal Art, Nanjing Normal University（南京师范大学文学院）；School of Information Management, Nanjing University（南京大学信息管理学院）
King’s College London（伦敦国王学院）；University of Oxford（牛津大学）
Distributional semantics, the quantitative study of meaning variation and change through corpus collocations, is currently one of the most productive research areas in computational linguistics. The wider availability of big data and of reproducible algorithms for analysis has boosted its application to living languages in recent years. But can we use distributional semantics to study a language with such a limited corpus as ancient Greek? And can this approach tell us something about such vexed questions in classical studies as the language and composition of the Homeric poems? Our paper will…
Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination
NLP2 CT Lab, Department of Computer and Information Science, University of Macau（澳门大学自然语言处理与计算机实验室，计算机与信息科学系）；Institute of International Language Services Studies, Macau Millennium College（澳门 millennium 学院国际语言服务研究学院）
Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions abo…
