Abstract Named Entity Recognition (NER), one of the most fundamental problems in natural
language processing, seeks to identify the boundaries and types of entities with specific …

Research on sentiment analysis in English language has undergone major developments in
recent years. Chinese sentiment analysis research, however, has not evolved significantly …

Recent pretraining models in Chinese neglect two important aspects specific to the Chinese
language: glyph and pinyin, which carry significant syntax and semantic information for …

Abstract Clinical Named Entity Recognition (CNER) is a critical task which aims to identify
and classify clinical terms in electronic medical records. In recent years, deep neural …

State-of-the-art systems of Chinese Named Entity Recognition (CNER) require large
amounts of hand-crafted features and domain-specific knowledge to achieve high …

It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the
use of the glyph information in those languages. However, due to the lack of rich …

Aspect-based sentiment analysis aims at identifying sentiment polarity towards aspect
targets in a sentence. Previously, the task was modeled as a sentence-level sentiment …

Word embeddings have attracted much attention recently. Different from alphabetic writing
systems, Chinese characters are often composed of subcharacter components which are …

This paper considers the problem of learning Chinese word embeddings. In contrast to
English, a Chinese word is usually composed of characters, and most of the characters …

Named entity recognition is a fundamental and crucial task in medical natural language
processing problems. In medical fields, Chinese clinical named entity recognition identifies …