Measuring Topic Coherence through Optimal Word Buckets
Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification. To measure quality of Latent Dirichlet Allocation (LDA) based topics learned from text, we propose a novel approach based on grouping of topic words into buckets (TBuckets). A single large bucket signifies a single coherent theme, in turn indicating high topic coherence. TBuckets uses word embeddings of topic words and employs singular value decomposition (SVD) and Integer Linear Programming based optimization to create coherent word buckets. TBuckets outperforms the state-of-the-art techniques when evaluated using 3 publicly available datasets and on another one proposed in this paper.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationInformation RetrievalRetrievaltext-classificationText ClassificationTopic ModelsWord EmbeddingsSimilar Papers 제목 키워드 기반
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three elements for assessing diversity:…
DiversityTopic ModelsEvaluating topic coherence measures
Topic models extract representative word sets - called topics - from word counts in documents without requiring any semantic annotations. Topics are not guaranteed to be well interpretable, therefore, coherence measures …
PhilosophyTopic ModelsTopical Coherence in LDA-based Models through Induced Segmentation
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a co…
Ad-Hoc Information RetrievalGeneral ClassificationInformation Retrievaltext-classification+2SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining
Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used t…
Opinion MiningTopic ModelsOTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
We present an optimal transport framework for learning topics from textual data. While the celebrated Latent Dirichlet allocation (LDA) topic model and its variants have been applied to many disciplines, they mainly focu…