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A Machine Learning Approach to Coreference Resolution of Noun Phrases
TLDR
The learning approach to coreference resolution of noun phrases in unrestricted text is presented, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Expand
Towards Robust Linguistic Analysis using OntoNotes
TLDR
An analysis of the performance of publicly available, state-of-the-art tools on all layers and languages in the OntoNotes v5.0 corpus should set the benchmark for future development of various NLP components in syntax and semantics, and possibly encourage research towards an integrated system that makes use of the various layers jointly to improve overall performance. Expand
Recognizing Implicit Discourse Relations in the Penn Discourse Treebank
TLDR
An implicit discourse relation classifier is presented in the Penn Discourse Treebank that considers the context of the two arguments, word pair information, as well as the arguments' internal constituent and dependency parses. Expand
A PDTB-styled end-to-end discourse parser
TLDR
This work has designed and developed an end-to-end discourse parser- to-parse free texts in the PDTB style in a fully data-driven approach and significantly improves on the current state-of-the-art connective classifier. Expand
An Unsupervised Neural Attention Model for Aspect Extraction
TLDR
Methods, systems, and computer-readable storage media for receiving a vocabulary, the vocabulary including text data that is provided as at least a portion of raw data, and the trained neural attention model being used to automatically determine aspects from the vocabulary. Expand
Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English
TLDR
The annotation schema and the data collection and annotation process of NUCLE are described and an unpublished study of annotator agreement for grammatical error correction is reported on. Expand
Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
TLDR
This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. Expand
It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text
TLDR
The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers, and it achieves state-of-the-art results on several SensEval and SemEval tasks. Expand
The CoNLL-2013 Shared Task on Grammatical Error Correction
TLDR
The task definition is given, the data sets are presented, and the evaluation metric and scorer used in the shared task are described, to give an overview of the various approaches adopted by the participating teams, and present the evaluation results. Expand
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