Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency
[Purpose/significance] The research on topic recognition is very important to clarify the knowledge structure and research hotspots in the field. Dynamic identification of domain topics can help researchers understand and master the development trend and future trend of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
LIS Press
2022-03-01
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| Series: | Zhishi guanli luntan |
| Subjects: | |
| Online Access: | http://kmf.ac.cn/p/284/ |
| _version_ | 1818271869098262528 |
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| author | Xi Chongjun Liu Wenbin Ding Kai |
| author_facet | Xi Chongjun Liu Wenbin Ding Kai |
| author_sort | Xi Chongjun |
| collection | DOAJ |
| description | [Purpose/significance] The research on topic recognition is very important to clarify the knowledge structure and research hotspots in the field. Dynamic identification of domain topics can help researchers understand and master the development trend and future trend of the field. [Method/process] Using the data structure form of tensor, this paper integrated the time dimension into the word co-occurrence matrix, and only needed one clustering to identify the dynamic topic. [Result/conclusion] Tensor structure and non-negative tensor decomposition algorithm provide a new method for dynamic topic recognition from the perspective of word co-occurrence frequency change. Compared with traditional methods, this method is simpler and faster, and effectively avoids the loss of information. |
| first_indexed | 2024-12-12T21:33:01Z |
| format | Article |
| id | doaj.art-dd7dba5bd0c04d3bb00f5cd11ba41369 |
| institution | Directory Open Access Journal |
| issn | 2095-5472 |
| language | zho |
| last_indexed | 2024-12-12T21:33:01Z |
| publishDate | 2022-03-01 |
| publisher | LIS Press |
| record_format | Article |
| series | Zhishi guanli luntan |
| spelling | doaj.art-dd7dba5bd0c04d3bb00f5cd11ba413692022-12-22T00:11:17ZzhoLIS PressZhishi guanli luntan2095-54722022-03-01720010.13266/j.issn.2095-5472.2022.017Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency Xi ChongjunLiu WenbinDing Kai[Purpose/significance] The research on topic recognition is very important to clarify the knowledge structure and research hotspots in the field. Dynamic identification of domain topics can help researchers understand and master the development trend and future trend of the field. [Method/process] Using the data structure form of tensor, this paper integrated the time dimension into the word co-occurrence matrix, and only needed one clustering to identify the dynamic topic. [Result/conclusion] Tensor structure and non-negative tensor decomposition algorithm provide a new method for dynamic topic recognition from the perspective of word co-occurrence frequency change. Compared with traditional methods, this method is simpler and faster, and effectively avoids the loss of information.http://kmf.ac.cn/p/284/keyword co-occurrence |
| spellingShingle | Xi Chongjun Liu Wenbin Ding Kai Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency Zhishi guanli luntan keyword co-occurrence |
| title | Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency |
| title_full | Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency |
| title_fullStr | Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency |
| title_full_unstemmed | Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency |
| title_short | Research on Dynamic Topic Recognition Based on the Change of Word Co-Occurrence Frequency |
| title_sort | research on dynamic topic recognition based on the change of word co occurrence frequency |
| topic | keyword co-occurrence |
| url | http://kmf.ac.cn/p/284/ |
| work_keys_str_mv | AT xichongjun researchondynamictopicrecognitionbasedonthechangeofwordcooccurrencefrequency AT liuwenbin researchondynamictopicrecognitionbasedonthechangeofwordcooccurrencefrequency AT dingkai researchondynamictopicrecognitionbasedonthechangeofwordcooccurrencefrequency |