Using Finite State Technology in Natural Language Processing of Basque

Alegria, Iñaki; Aranzabe, Maxux; Ezeiza, Nerea; Ezeiza, Aitzol; Urizar, Ruben

doi:10.1007/3-540-36390-4_1

Cited by 8 publications

(2 citation statements)

References 20 publications

(17 reference statements)

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“…1996), (ii) A lemmatiser that also disambiguates the PoS and the syntactic function (Alegria et al . 2002), (iii) A multi-word item identifier that determines which groups of two or more words are to be considered multi-word expressions (Alegria et al . 2004), (iv) A named-entity recogniser that identifies and classifies named entities (person, organization, location) in the text (Alegria et al .…”

Section: Methodsmentioning

confidence: 99%

Improving mention detection for Basque based on a deep error analysis

Soraluze

Arregi

et al. 2016

Nat. Lang. Eng.

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This paper presents the improvement process of a mention detector for Basque. The system is rule-based and takes into account the characteristics of mentions in Basque. A classification of error types is proposed based on the errors that occur during mention detection. A deep error analysis distinguishing error types and causes is presented and improvements are proposed. At the final stage, the system obtains an F-measure of 74.57% under the Exact Matching protocol and of 80.57% under Lenient Matching. We also show the performance of the mention detector with gold standard data as input, in order to omit errors caused by the previous stages of linguistic processing. In this scenario, we obtain an F-measure of 85.89% with Strict Matching and of 89.06% with Lenient Matching, i.e., a difference of 11.32 and 8.49 percentage points, respectively. Finally, how improvements in mention detection affect coreference resolution is analysed.

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Section: Methodsmentioning

confidence: 99%

Improving mention detection for Basque based on a deep error analysis

Soraluze

Arregi

et al. 2016

Nat. Lang. Eng.

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“…Unitex allows the use of graph cascade with the CasSys program inspired from [16]. Cascades [17] are used in many NLP applications, as chunking [18], syntactic analysis [19], morphological analysis [20] and so on.…”

Section: Unitex Softwarementioning

confidence: 99%

Istex: A Database of Twenty Million Scientific Papers with a Mining Tool Which Uses Named Entities

et al. 2019

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Istex is a database of twenty million full text scientific papers bought by the French Government for the use of academic libraries. Papers are usually searched for by the title, authors, keywords or possibly the abstract. To authorize new types of queries of Istex, we implemented a system of named entity recognition on all papers and we offer users the possibility to run searches on these entities. After the presentation of the French Istex project, we detail in this paper the named entity recognition with CasEN, a cascade of graphs, implemented on the Unitex Software. CasEN exists in French, but not in English. The first challenge was to build a new cascade in a short time. The results of its evaluation showed a good Precision measure, even if the Recall was not very good. The Precision was very important for this project to ensure it did not return unwanted papers by a query. The second challenge was the implementation of Unitex to parse around twenty millions of documents. We used a dockerized application. Finally, we explain also how to query the resulting Named entities in the Istex website.

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