ERIC Number: ED596349
Record Type: Non-Journal
Publication Date: 2016
Pages: 2
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
Can Word Probabilities from LDA Be Simply Added up to Represent Documents?
Cai, Zhiqiang; Li, Hiyiang; Hu, Xiangen; Graesser, Art
Grantee Submission, Paper presented at the International Conference on Educational Data Mining (9th, Raleigh, NC, Jun 29-Jul 2, 2016)
This paper provides an alternative way of document representation by treating topic probabilities as a vector representation for words and representing a document as a combination of the word vectors. A comparison on summary data shows that this representation is more effective in document classification. [This paper was published in: "Proceedings of the 9th International Conference on Educational Data Mining" (p577-578).]
Descriptors: Probability, Natural Language Processing, Models, Automation, Documentation, Cluster Grouping, Data Analysis
Publication Type: Speeches/Meeting Papers; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); Institute of Education Sciences (ED); US Army Research Laboratory (ARL); Office of Naval Research (ONR)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: DRK120918409; 1108845; R305H050169; R305B070349; R305A080589; R305A080594; R305G020018; R305C120001; W911INF1220030; N000140010600; N0001412C0643