Pregled bibliografske jedinice broj: 1143675
A Topic Coverage Approach to Evaluation of Topic Models
A Topic Coverage Approach to Evaluation of Topic Models // IEEE access, 9 (2021), 123280-123312 doi:10.1109/access.2021.3109425 (međunarodna recenzija, članak, znanstveni)
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Naslov
A Topic Coverage Approach to Evaluation of Topic Models
Autori
Korenčić, Damir ; Ristov, Strahil ; Repar, Jelena ; Šnajder, Jan
Izvornik
IEEE access (2169-3536) 9
(2021);
123280-123312
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Topic coverage ; Topic coherence ; Topic discovery ; Topic models ; Topic model evaluation ; Topic model stability
Sažetak
Topic models are widely used unsupervised models capable of learning topics – weightedlists of words and documents – from large collections of text documents. When topic models are used fordiscovery of topics in text collections, a question that arises naturally is how well the model-induced topicscorrespond to topics of interest to the analyst. In this paper we revisit and extend a so far neglected approachto topic model evaluation based on measuring topic coverage – computationally matching model topics witha set of reference topics that models are expected to uncover. The approach is well suited for analyzingmodels’ performance in topic discovery and for large-scale analysis of both topic models and measures ofmodel quality. We propose new measures of coverage and evaluate, in a series of experiments, different typesof topic models on two distinct text domains for which interest for topic discovery exists. The experimentsinclude evaluation of model quality, analysis of coverage of distinct topic categories, and the analysis of therelationship between coverage and other methods of topic model evaluation. The paper contributes a newsupervised measure of coverage, and the first unsupervised measure of coverage. The supervised measureachieves topic matching accuracy close to human agreement. The unsupervised measure correlates highlywith the supervised one (Spearman’s ρ≥ 0.95). Other contributions include insights into both topic modelsand different methods of model evaluation, and the datasets and code for facilitating future research on topiccoverage.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Institut "Ruđer Bošković", Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus