Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection
This paper discusses how to adapt two new word embedding features to build a more efficient Chinese Grammatical Error Diagnosis (CGED) systems to assist Chinese foreign learners (CFLs) in improving their written essays. The major idea is to apply word order sensitive Word2Vec approaches including (1) structured skip-gram and (2) continuous window (CWindow) models, because they are more suitable for solving syntax-based problems. The proposed new features were evaluated on the Test of Chinese as a Foreign Language (TOCFL) learner database provided by NLP-TEA-3{\&}CGED shared task. Experimental results showed that the new features did work better than the traditional word order insensitive Word2Vec approaches. Moreover, according to the official evaluation results, our system achieved the lowest (0.1362) false positive (FA) and the highest precision rates in all three measurements.
Code (0)
등록된 구현이 없습니다.
Tasks
Grammatical Error DetectionLanguage ModellingSimilar Papers 제목 키워드 기반
Efficient, Compositional, Order-sensitive n-gram Embeddings
We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddin…
Word EmbeddingsWord Ordering as Unsupervised Learning Towards Syntactically Plausible Word Representations
The research question we explore in this study is how to obtain syntactically plausible word representations without using human annotations. Our underlying hypothesis is that word ordering tests, or linearizations, is s…
Dependency ParsingPart-Of-Speech TaggingWord EmbeddingsOn the Effect of Word Order on Cross-lingual Sentiment Analysis
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional ne…
Cross-Lingual Sentiment ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+3What are the biases in my word embedding?
This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embed…
Word EmbeddingsWord2rate: training and evaluating multiple word embeddings as statistical transitions
Using pretrained word embeddings has been shown to be a very effective way in improving the performance of natural language processing tasks. In fact almost any natural language tasks that can be thought of has been impr…
Sentiment AnalysisTranslationWord Embeddings