2007 CoNLL Shared Task - Arabic & English
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Tâche partagée CoNLL 2007 – arabe et anglais
2007 CoNLL Shared Task - Arabic & English consists of dependency treebanks in two languages used as part of the CoNLL 2007 shared task on multi-lingual dependency parsing and domain adaptation. The languages covered in this release are: Arabic and English.
The Conference on Computational Natural Language Learning (CoNLL) is accompanied every year by a shared task intended to promote natural language processing applications and evaluate them in a standard setting. In 2006 and 2007, the shared task was devoted to the parsing of syntactic dependencies using corpora from up to thirteen languages. The task aimed to define and extend the then-current state of the art in dependency parsing, a technology that complemented previous tasks by producing a different kind of syntactic description of input text. The 2007 shared task added a domain adaptation track for English in addition to the multilingual track. More information about CoNLL and the 2007 shared task are available respectively at: http://www.signll.org/conll/ and http://www.conll.org/previous-tasks.
The source data in the treebanks in this release consists principally of various texts (e.g., textbooks, news, literature) annotated in dependency format. In general, dependency grammar is based on the idea that the verb is the center of the clause structure and that other units in the sentence are connected to the verb as directed links or dependencies. This is a one-to-one correspondence: for every element in the sentence there is one node in the sentence structure that corresponds to that element. In constituency or phrase structure grammars, on the other hand, clauses are divided into noun phrases and verb phrases and in each sentence, one or more nodes may correspond to one element. All of the data sets in this release are dependency treebanks.
The individual data sets are:
Prague Arabic Dependency Treebank (Arabic)
PennBioIE Oncology 1.0 (English)
PennBioIE CYP 1.0 (English)
Click here for licensing instructions: https://catalog.ldc.upenn.edu/LDC2017T21.
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