This study explores the feasibility of performing Chinese word segmentation (CWS) and
POS tagging by deep learning. We try to avoid task-specific feature engineering, and use …

Transition-based dependency parsers generally use heuristic decoding algorithms but can
accommodate arbitrarily rich feature representations. In this paper, we show that we can …

We study a range of syntactic processing tasks using a general statistical framework that
consists of a global linear model, trained by the generalized perceptron together with a …

Chinese word segmentation is an active area in Chinese language processing though it is
suffering from the argument about what precisely is a word in Chinese. Based on corpus …

This study explores the feasibility of integrating unsupervised and supervised segmentation
of Chinese texts for enhancing performance beyond the present state-of-the art, focusing on …

Conventional approaches to Chinese word segmentation treat the problem as a
characterbased tagging task. Recently, semi-Markov models have been applied to the …

Online shoppers now primarily rely on product reviews and user experiences shared on
social media platforms before making an online purchase. The opportunity to capture …

This paper presents a novel approach to Chinese word segmentation (CWS) that attempts to
utilize global information (GI) such as co-occurrence of sub-sequences and outputs of …

There are two dominant approaches to Chinese word segmentation: word-based and
character-based models, each with respective strengths. Prior work has shown that gains in …

This paper presents a novel approach to improve Chinese word segmentation (CWS) that
attempts to utilize unlabeled data such as training and test data without annotation for further …