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The CoNLL 2007 Shared Task on Dependency Parsing
The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the sharedExpand
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Transfer Learning for Low-Resource Neural Machine Translation
The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transferExpand
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SemEval-2007 Task 04: Classification of Semantic Relations between Nominals
The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of relations between pairs of words in a text. We present an evaluation task designed toExpand
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Discovery of linguistic relations using lexical attraction
This work has been motivated by two long term goals: to understand how humans learn language and to build programs that can understand language. Using a representation that makes the relevantExpand
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Natural Language Communication with Robots
We propose a framework for devising empirically testable algorithms for bridging the communication gap between humans and robots. We instantiate our framework in the context of a problem setting inExpand
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Omnibase: Uniform Access to Heterogeneous Data for Question Answering
Although the World Wide Web contains a tremendous amount of information, the lack of uniform structure makes finding the right knowledge difficult. A solution is to turn the Web into a "virtualExpand
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CharNER: Character-Level Named Entity Recognition
We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consistsExpand
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Learning Morphological Disambiguation Rules for Turkish
In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm using supervised training. MorphologicalExpand
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Instance Selection for Machine Translation using Feature Decay Algorithms
We present an empirical study of instance selection techniques for machine translation. In an active learning setting, instance selection minimizes the human effort by identifying the mostExpand
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SemEval-2010 Task 12: Parser Evaluation Using Textual Entailments
Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based onExpand
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