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Detection of Grammatical Errors Involving Prepositions
TLDR
A maximum entropy classifier combined with rule-based filters are used to detect preposition errors in a corpus of student essays to help develop an NLP application that can reliably detect these types of errors.
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Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System
TLDR
This paper trains a classifier on a large-scale, error-tagged corpus of English essays written by ESL learners, relying on contextual and grammatical features surrounding preposition usage, and shows that this model outperforms models trained on well-edited text produced by native speakers of English.
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Detecting errors in English article usage by non-native speakers
TLDR
A maximum entropy classifier was trained to select among a/an, the, or zero article for noun phrases (NPs), based on a set of features extracted from the local context of each, and used to detect article errors in TOEFL essays of native speakers of Chinese, Japanese, and Russian.
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Building Universal Dependency Treebanks in Korean
This paper presents three treebanks in Korean that consist of dependency trees derived from existing treebanks, the Google UD Treebank, the Penn Korean Treebank, and the KAIST Treebank, and…
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Penn Korean Treebank : Development and Evaluation
TLDR
Some issues in developing the annotation guidelines for POS tagging and syntactic bracketing are discussed, including various methods used to detect and correct annotation errors, and some statistics on the size of the corpus are presented.
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Development and Evaluation of a Korean Treebank and its Application to NLP
TLDR
Issues involved in building a 54-thousand-word Korean Treebank using a phrase structure annotation and the development of annotation guidelines based on the morpho-syntactic phenomena represented in the corpus are discussed.
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Guidelines for Penn Korean Treebank Version 2.0
TLDR
This document outlines the guidelines that were newly introduced for the second volume of the Penn Korean Treebank, as well as the ones that have been revised since the publication of volume 1.0.
Detecting Errors in English Article Usage with a Maximum Entropy Classifier Trained on a Large, Diverse Corpus
TLDR
A maximum entropy classifier was trained to select among a/an, the, or zero article for noun phrases, based on a set of features extracted from the local context of each, which was correct about 88% of the time.
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Korean zero pronouns: analysis and resolution
Zero pronouns, or dropped arguments, are a remarkably frequent phenomenon in Korean. This single syntactic form is made up of diverse subcategories, each of which is characterized by distinct…
Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
TLDR
Traditional and deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing as well as supporting the development of a vaping surveillance system.
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