Topic Representation using Semantic-Based Patterns
| Autor: | Yue Xu, Yuefeng Li, Dakshi Tharanga Kapugama Geeganage |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Topic model
Perplexity Information retrieval Computer science Probabilistic logic 020207 software engineering 02 engineering and technology Ontology (information science) Semantics Set (abstract data type) 020204 information systems 0202 electrical engineering electronic engineering information engineering Representation (mathematics) Coherence (linguistics) |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811516986 AusDM |
| DOI: | 10.1007/978-981-15-1699-3_3 |
| Popis: | Topic modelling is the state of the art technique for understanding, organizing, and extracting information from text collections. Traditional topic modeling approaches apply probabilistic techniques to generate the list of topics from collections. Nevertheless, human understands, summarizes and discovers the topics based on the meaning of the content. Hence, the quality of the topic models can be improved by grasping the meaning from the content. In this paper, we propose an approach to identify sets of meaningful terms based on ontology, called Semantic-based Patterns, which represent the content of a collection of documents. A set of related semantic-based patterns can be used to represent a latent topic in the collection. The proposed Topic Representation using Semantic-based Patterns aims to generate semantically meaningful patterns based on ontology rather than term co-occurrence as what existing topic modelling methods do. The semantically meaningful patterns were evaluated by applying the information filtering to semantic-based topic representation. The semantic based patterns were used as features for information filtering and were evaluated by comparing against popular information filtering baseline systems. Topic quality was evaluated in terms of topic coherence and perplexity. The experimental results verified that the quality of the proposed patterns was better than features used in baseline systems for information filtering. Further, the quality of topic representation outperforms the generated topics of other topic modeling approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|