Do Dinh, Erik-Lân ; Eckart de Castilho, Richard ; Gurevych, Iryna (2015):
In-tool Learning for Selective Manual Annotation in Large Corpora.
In: Proceedings of ACL-IJCNLP 2015 System Demonstrations, pp. 13-18,
Association for Computational Linguistics and The Asian Federation of Natural Language Processing, Beijing, China, [Conference or Workshop Item]
Abstract
We present a novel approach to the selective annotation of large corpora through the use of machine learning. Linguistic search engines used to locate potential instances of an infrequent phenomenon do not support ranking of the search results. This favors the use of high-precision queries that return only a few results over broader queries that have a higher recall. Our approach introduces a classifier used to rank the search results and thus helping the annotator focus on those results with the highest potential of being an instance of the phenomenon in question, even in low-precision queries. The classifier is trained in an in-tool fashion, except for preprocessing relying only on the manual annotations done by the users in the querying tool itself. To implement this approach, we build upon an existing web-based multi-user search and annotation tool.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2015 |
| Creators: | Do Dinh, Erik-Lân ; Eckart de Castilho, Richard ; Gurevych, Iryna |
| Title: | In-tool Learning for Selective Manual Annotation in Large Corpora |
| Language: | English |
| Abstract: | We present a novel approach to the selective annotation of large corpora through the use of machine learning. Linguistic search engines used to locate potential instances of an infrequent phenomenon do not support ranking of the search results. This favors the use of high-precision queries that return only a few results over broader queries that have a higher recall. Our approach introduces a classifier used to rank the search results and thus helping the annotator focus on those results with the highest potential of being an instance of the phenomenon in question, even in low-precision queries. The classifier is trained in an in-tool fashion, except for preprocessing relying only on the manual annotations done by the users in the querying tool itself. To implement this approach, we build upon an existing web-based multi-user search and annotation tool. |
| Book Title: | Proceedings of ACL-IJCNLP 2015 System Demonstrations |
| Publisher: | Association for Computational Linguistics and The Asian Federation of Natural Language Processing |
| Uncontrolled Keywords: | Knowledge Discovery in Scientific Literature;UKP_a_LangTech4eHum;UKP_s_CSniper;UKP_reviewed |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources |
| Event Location: | Beijing, China |
| Date Deposited: | 31 Dec 2016 14:29 |
| URL / URN: | http://www.aclweb.org/anthology/P15-4003 |
| Identification Number: | TUD-CS-2015-0098 |
| PPN: | |
| Corresponding Links: | |
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