Normal view MARC view ISBD view

Assigning inflectional paradigms to named entities by linear successive abstraction / Ljubešić, Nikola ; Bakarić, Nikola ; Lauc, Tomislava.

By: Ljubešić, Nikola, informatičar.
Contributor(s): Bakarić, Nikola [aut] | Lauc, Tomislava [aut].
Material type: ArticleArticleDescription: 190-193 str.Other title: Assigning Inflectional Paradigms to Named Entities by Linear Successive Abstraction [Naslov na engleskom:].Subject(s): 5.04 | inflectional morphology, supervised learning, linear successive abstraction, morphological paradigm assignment, named entity hrv | inflectional morphology, supervised learning, linear successive abstraction, morphological paradigm assignment, named entity eng In: 31st International Convention on Information and Communication Technology, Electronics and Microelectronics (26-30.5.2008. ; Opatija, Hrvatska) Proceedings Vol.III. MIPRO 2008. Computers in Technical Systems & Intelligent Systems (CTS & CIS) str. 190-193Bogunović, Nikola ; Ribarić, SlobodanSummary: This paper describes how a supervised learning method is used for assigning inflectional paradigms to organization entity names as the main prerequisite for generating a morphological lexicon of these named entities. An inflectional paradigm consists of a set of rules for generating all forms of a lexicon entry. A morphological lexicon consists of lexicon entries and their corresponding forms. This type of language resource is crucial in tasks such as natural language generation (generating natural language business news from database data and news templates) and named entity identification (necessary step in data mining and business intelligence). The basic resource used in this research is a list of 106, 530 named entities of organizations given in basic form (nominative case) and ranked by relevance. On the first 5, 000 manually tagged named entities 59 inflectional paradigm classes are defined. Using linear successive abstraction, a suffix model is trained, validated and tested on this tagged dataset. Morphological lexica of general language, personal names and settlements are used as additional resources in the decision process. The achieved accuracy on the test set is 98.70%.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

This paper describes how a supervised learning method is used for assigning inflectional paradigms to organization entity names as the main prerequisite for generating a morphological lexicon of these named entities. An inflectional paradigm consists of a set of rules for generating all forms of a lexicon entry. A morphological lexicon consists of lexicon entries and their corresponding forms. This type of language resource is crucial in tasks such as natural language generation (generating natural language business news from database data and news templates) and named entity identification (necessary step in data mining and business intelligence). The basic resource used in this research is a list of 106, 530 named entities of organizations given in basic form (nominative case) and ranked by relevance. On the first 5, 000 manually tagged named entities 59 inflectional paradigm classes are defined. Using linear successive abstraction, a suffix model is trained, validated and tested on this tagged dataset. Morphological lexica of general language, personal names and settlements are used as additional resources in the decision process. The achieved accuracy on the test set is 98.70%.

Projekt MZOS 130-1301679-1380

Projekt MZOS 130-1301799-1999

ENG

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha

//