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Works212 from public data
- A Machine Learning Approach to Coreference Resolution of Noun Phrases1,146
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.
- The CoNLL-2014 Shared Task on Grammatical Error Correction592
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.
- Feature selection, perception learning, and a usability case study for text categorization588
An automated learning approach to text categorization based on perception learning and a new feature selection metric, called correlation coefficient, is described and empirical results indicate that this approach outperforms the best published results on this % uters collection.
- A Neural Approach to Automated Essay Scoring570
This paper develops an approach based on recurrent neural networks to learn the relation between an essay and its assigned score, without any feature engineering.
- Integrating multiple knowledge sources to disambiguate word sense569
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.
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- Towards Robust Linguistic Analysis using OntoNotes521
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.
- An Unsupervised Neural Attention Model for Aspect Extraction431
A novel neural approach that improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings, and uses an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects.
- Better Evaluation for Grammatical Error Correction420
This work presents a novel method for evaluating grammatical error correction that is an algorithm for efficiently computing the sequence of phrase-level edits between a source sentence and a system hypothesis that achieves the highest overlap with the gold-standard annotation.
- Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English407
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.
- The CoNLL-2013 Shared Task on Grammatical Error Correction357
The CoNLL-2013 shared task was devoted to grammatical error correction and the task definition is given, the data sets are presented, and the evaluation metric and scorer used are described.
- It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text355
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.
- Named entity recognition337
It is shown that the maximum entropy framework is able to make use of global information directly, and achieves performance that is comparable to the best previous machine learning-based NERs on M UC-6 and MUC-7 test data.
- Word Sense Disambiguation Improves Statistical Machine Translation329
It is shown for the first time that integrating a WSD system improves the performance of a state-of-the-art statistical MT system on an actual translation task, and the improvement is statistically significant.
- Recognizing implicit discourse relations in the Penn Discourse Treebank313
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.
- A PDTB-styled end-to-end discourse parser302
This work designed and developed an end-to-end discourse parser-to-parse free texts in the PDTB style in a fully data-driven approach and proposes and presents a comprehensive evaluation from both component-wise and error-cascading perspectives.
- An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation286
Evaluated knowledge sources include the part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations, and the SVM, Naive Bayes, AdaBoost, and decision tree algorithms.
- An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis282
An interactive multi-task learning network (IMN) is proposed which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level and introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables.
- Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction279
A representation scheme for relation tuples which enables the decoder to generate one word at a time like machine translation models and still finds all the tuples present in a sentence with full entity names of different length and with overlapping entities.
- Named entity recognition with a maximum entropy approach275
The named entity recognition (NER) task involves identifying noun phrases that are names, and assigning a class to each name.
- A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction239
By ensembling multiple models, and incorporating an N-gram language model and edit features via rescoring, this novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach, both in terms of grammaticality and fluency.
- Exploiting Document Knowledge for Aspect-level Sentiment Classification220
Two approaches that transfer knowledge from document-level data, which is much less expensive to obtain, are explored to improve the performance of aspect-level sentiment classification.
- Effective Attention Modeling for Aspect-Level Sentiment Classification212
This work proposes a method for target representation that better captures the semantic meaning of the opinion target and introduces an attention model that incorporates syntactic information into the attention mechanism.
- Chinese Part-of-Speech Tagging: One-at-a-Time or All-at-Once? Word-Based or Character-Based?199
An in-depth study on issues of processing architecture and feature representation for Chinese POS tagging, within a maximum entropy framework, and builds a state-of-the-art Chinese word segmenter, which outperforms the best SIGHAN 2003 word segmenters in the closed track on 3 out of 4 test corpora.
- Exploiting parallel texts for word sense disambiguation182
This paper evaluates an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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