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- Nivre, Joakim, 1962-, et al.
(författare)
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Cross-Framework Evaluation for Statistical Parsing
- 2012
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Ingår i: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL). - : Association for Computational Linguistics. - 9781937284190
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Konferensbidrag (refereegranskat)
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| 2. |
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| 3. |
- Nivre, Joakim, 1962-, et al.
(författare)
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Joint Evaluation of Morphological Segmentation and Syntactic Parsing
- 2012
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Ingår i: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). - : Association for Computational Linguistics. - 9781937284251 ; , s. 6-10
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Konferensbidrag (refereegranskat)
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| 4. |
- Nivre, Joakim, 1962-, et al.
(författare)
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Universal Dependencies v1 : A Multilingual Treebank Collection
- 2016
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Ingår i: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). - Paris : EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. - 9782951740891 ; , s. 1659-1666
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Konferensbidrag (refereegranskat)abstract
- Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.
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| 5. |
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| 6. |
- Seddah, Djamé, et al.
(författare)
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Overview of the SPMRL 2013 Shared Task : A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
- 2013
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Ingår i: Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages. - : Association for Computational Linguistics. - 9781937284978 ; , s. 146-182
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios.
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| 7. |
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| 8. |
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| 9. |
- Tsarfaty, Reut, et al.
(författare)
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Parsing Morphologically Rich Languages : Introduction to the Special Issue
- 2013
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Ingår i: Computational linguistics - Association for Computational Linguistics (Print). - 0891-2017 .- 1530-9312. ; 39:1, s. 15-22
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Tidskriftsartikel (refereegranskat)abstract
- Parsing is a key task in natural language processing. It involves predicting, for each natural language sentence, an abstract representation of the grammatical entities in the sentence and the relations between these entities. This representation provides an interface to compositional semantics and to the notions of "who did what to whom." The last two decades have seen great advances in parsing English, leading to major leaps also in the performance of applications that use parsers as part of their backbone, such as systems for information extraction, sentiment analysis, text summarization, and machine translation. Attempts to replicate the success of parsing English for other languages have often yielded unsatisfactory results. In particular, parsing languages with complex word structure and flexible word order has been shown to require non-trivial adaptation. This special issue reports on methods that successfully address the challenges involved in parsing a range of morphologically rich languages (MRLs). This introduction characterizes MRLs, describes the challenges in parsing MRLs, and outlines the contributions of the articles in the special issue. These contributions present up-to-date research efforts that address parsing in varied, cross-lingual settings. They show that parsing MRLs addresses challenges that transcend particular representational and algorithmic choices.
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