Exploring the Space of Topic Coherence Measures
Bibliographic Data
| ID | 23316993 |
|---|---|
| Authors | Michael Røder (0000-0002-8609-8277, Leipzig University), Andreas Both (0000-0002-9177-5463, R&D, Unister GmbH, Leipzig, Germany), Alexander Hinneburg (Martin Luther University Halle-Wittenberg) |
| Year | 2015 |
| Pages | 399-408 |
| Publication date | 2015-02-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | CONFERENCE |
| Venue | Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (CONFERENCE) |
| Publisher | ACM (PUBLISHER) |
| DOI | 10.1145/2684822.2685324 |
| OpenAlex | W2038043464 |
| Language | EN |
| Citations received | 204 |
| References cited | 12 |
Quantifying the coherence of a set of statements is a long standing problem with many potential applications that has attracted researchers from different sciences. The special case of measuring coherence of topics has been recently studied to remedy the problem that topic models give no guaranty on the interpretablity of their output. Several benchmark datasets were produced that record human judgements of the interpretability of topics. We are the first to propose a framework that allows to construct existing word based coherence measures as well as new ones by combining elementary components. We conduct a systematic search of the space of coherence measures using all publicly available topic relevance data for the evaluation. Our results show that new combinations of components outperform existing measures with respect to correlation to human ratings. nFinally, we outline how our results can be transferred to further applications in the context of text mining, information retrieval and the world wide web.
Coherence (philosophical gambling strategy) · Construct (python library) · Context (archaeology) · Data mining · Data science · Information retrieval · Interpretability · Natural language processing · Relevance (law) · Set (abstract data type) · Space (punctuation) · Advanced Text Analysis Techniques · Artificial Intelligence · Computer Science · Mathematics · Natural Language Processing Techniques · Topic Modeling
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Low-Code excellence
| Unique citing works | 204 |
|---|---|
| Citations per year | 22,67 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 169 |