Do Dinh, Erik-Lân ; Eckart de Castilho, Richard ; Gurevych, Iryna (2015)
In-tool Learning for Selective Manual Annotation in Large Corpora.
Beijing, China
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Do Dinh, Erik-Lân ; Eckart de Castilho, Richard ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | In-tool Learning for Selective Manual Annotation in Large Corpora |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2015 |
| Verlag: | Association for Computational Linguistics and The Asian Federation of Natural Language Processing |
| Buchtitel: | Proceedings of ACL-IJCNLP 2015 System Demonstrations |
| Veranstaltungsort: | Beijing, China |
| URL / URN: | http://www.aclweb.org/anthology/P15-4003 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Knowledge Discovery in Scientific Literature;UKP_a_LangTech4eHum;UKP_s_CSniper;UKP_reviewed |
| ID-Nummer: | TUD-CS-2015-0098 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum