作者主题模型及其改进的方法与应用研究综述

[目的/意义]作者主题模型作为近年来计算机领域关注度较高的新型概率模型，在文本挖掘与自然语言处理等方向已有广泛应用。分析国内外作者主题模型及其改进的思路与应用，更好地把握其研究现状，以期为计算机、图书情报等相关领域科研人员提供参考。[方法/过程]本文选取Web of Science核心数据库、DBLP及中国知网（CNKI）数据库作为文献来源，通过制定检索规则、去重及人工判读等操作提炼出关于作者主题模型及其改进方法的文献集，从模型应用过程的视角，结合文献分析法对现有研究进行总结归纳。[结果/结论]通过分析发现，现有相关研究已形成较为完整的分析流程，且模型的改进角度、适用领域也日益多样化。但性能优化、模型评价指标的规范完善以及在图书情报领域的进一步应用等方面仍有待深入探索。</p

Latent Topic Model Based Representations for a Robust Theme Identification of Highly Imperfect Automatic Transcriptions

International audienceSpeech analytics suffer from poor automatic transcription quality. To tackle this difficulty, a solution consists in mapping transcriptions into a space of hidden topics. This abstract representation allows to work around drawbacks of the ASR process. The well-known and commonly used one is the topic-based representation from a Latent Dirichlet Allocation (LDA). During the LDA learning process, distribution of words into each topic is estimated automatically. Nonetheless, in the context of a classification task, LDA model does not take into account the targeted classes. The supervised Latent Dirichlet Allocation (sLDA) model overcomes this weakness by considering the class, as a response, as well as the document content itself. In this paper, we propose to compare these two classical topic-based representations of a dialogue (LDA and sLDA), with a new one based not only on the dialogue content itself (words), but also on the theme related to the dialogue. This original Author-topic Latent Variables (ATLV) representation is based on the Author-topic (AT) model. The effectiveness of the proposed ATLV representation is evaluated on a classification task from automatic dialogue transcriptions of the Paris Transportation customer service call. Experiments confirmed that this ATLV approach outperforms by far the LDA and sLDA approaches, with a substantial gain of respectively 7.3 and 5.8 points in terms of correctly labeled conversations