Abstract
Segmenting text into semantically coherent segments is an important task with applications in information retrieval and text summarization. Developing accurate topical segmentation requires the availability of training data with ground truth information at the segment level. However, generating such labeled datasets, especially for applications in which the meaning of the labels is user-defined, is expensive and time-consuming. In this paper, we develop an approach that instead of using segment-level ground truth information, it instead uses the set of labels that are associated with a document and are easier to obtain as the training data essentially corresponds to a multilabel dataset. Our method, which can be thought of as an instance of distant supervision, improves upon the previous approaches by exploiting the fact that consecutive sentences in a document tend to talk about the same topic, and hence, probably belong to the same class. Experiments on the text segmentation task on a variety of datasets show that the segmentation produced by our method beats the competing approaches on four out of five datasets and performs at par on the fifth dataset. On the multilabel text classification task, our method performs at par with the competing approaches, while requiring significantly less time to estimate than the competing approaches.
Original language | English (US) |
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Title of host publication | 2018 IEEE International Conference on Data Mining, ICDM 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1170-1175 |
Number of pages | 6 |
ISBN (Electronic) | 9781538691588 |
DOIs | |
State | Published - Dec 27 2018 |
Event | 18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore Duration: Nov 17 2018 → Nov 20 2018 |
Publication series
Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
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Volume | 2018-November |
ISSN (Print) | 1550-4786 |
Conference
Conference | 18th IEEE International Conference on Data Mining, ICDM 2018 |
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Country/Territory | Singapore |
City | Singapore |
Period | 11/17/18 → 11/20/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Distant supervision
- Multilabel
- Segmentation