The Fips Multilingual Parser

Wehrli, Eric; Nerima, Luka

doi:10.1007/978-3-319-08043-7_26

Cited by 10 publications

(6 citation statements)

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“…It is released in three versions: plain texts of approximately six to eight million words per language, sentence-aligned bilingual texts for each language pair, and a PoS tagged version. The annotation has been performed automatically by the Fips multilingual parser [16]. AMARA corpus [17]: It is a parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e., 20 monolingual corpora and 190 parallel corpora.…”

Section: State Of the Artmentioning

confidence: 99%

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MulTed: a multilingual aligned and tagged parallel corpus

Zeroual

Lakhouaja

2020

ACI

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Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

MulTed: a multilingual aligned and tagged parallel corpus

Zeroual

Lakhouaja

2020

ACI

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“…Our system is a multilingual parser, available for several languages, i.e. French, English, German, Italian, Spanish, Modern Greek, Romanian and Portuguese (Wehrli, 2007;Wehrli and Nerima, 2015). It relies on generative grammar concepts and is basically made up of a generic parsing module which can be refined in order to suit the specific needs of a particular language.…”

Section: The Fips Parsermentioning

confidence: 99%

Parsing and MWE Detection: Fips at the PARSEME Shared Task

Nerima¹,

Foufi²,

Wehrli³

2017

Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

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Identifying multiword expressions (MWEs) in a sentence in order to ensure their proper processing in subsequent applications, like machine translation, and performing the syntactic analysis of the sentence are interrelated processes. In our approach, priority is given to parsing alternatives involving collocations, and hence collocational information helps the parser through the maze of alternatives, with the aim to lead to substantial improvements in the performance of both tasks (collocation identification and parsing), and in that of a subsequent task (machine translation). In this paper, we are going to present our system and the procedure that we have followed in order to participate to the open track of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs) in running texts.

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“…It is available on-line (http://www.latl.unige.ch) for the languages mentioned above. A detailed description of this parser and of its grammar model is beyond the scope of this paper and can be found in (Wehrli 2007;Wehrli and Nerima 2014). In a nutshell, Fips is a constituent parser, which means that it attempts to assign a constituent representation for a given input sentence, using grammatical concepts inspired by generative grammar, but largely and freely adapted to the task of efficient processing.…”

Section: The Fips Parsermentioning

confidence: 99%

Parsing Language-Specific Constructions: The Case of French Pronominal Clitics

Wehrli

2017

Formal Models in the Study of Language

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This paper describes the treatment of French pronominal clitics developed for the multilingual Fips parser. Following a brief description of the Fips parser with its object-oriented design and a short review of the basic facts about French clitics, an analysis is presented which distinguishes pronominal chains (in the unmarked case) and argument absorption (for reflexive-reciprocal clitics agreeing with the grammatical subject). Specific data structures and associated procedures are necessary to implement the clitic-trace chain in the first case and the argument absorption in the second case. Exploiting the object-oriented model, we argue that such specific mechanisms should not be part of the basic (universal) model of the parser, but rather should be introduced as a specialization of the base model for languages which display pronominal clitics, such as Romance languages.

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The Fips Multilingual Parser

Cited by 10 publications

References 10 publications

MulTed: a multilingual aligned and tagged parallel corpus

MulTed: a multilingual aligned and tagged parallel corpus

Parsing and MWE Detection: Fips at the PARSEME Shared Task

Parsing Language-Specific Constructions: The Case of French Pronominal Clitics

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