Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …

Text classification has been highlighted as the key process to organize online texts for better
communication in the Digital Media Age. Text classification establishes classification rules …

The representational basis for downstream natural language processing tasks is word
embeddings, which capture lexical semantics in numerical form to handle the abstract …

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 …

Clinic Named Entity Recognition (CNER) aims to recognize named entities such as body
part, disease and symptom from Electronic Health Records (EHRs), which can benefit many …

随着自然语言处理领域相关技术的快速发展, 作为自然语言处理的上游任务,
提高命名实体识别的准确率对于后续的文本处理任务而言具有重要的意义. 然而 …

High quality agricultural named entity recognition (NER) model can provide effective support
for agricultural information extraction, semantic retrieval and other tasks. However, the …

Learning sentence similarity is a fundamental research topic and has been explored using
various deep learning methods recently. In this paper, we further propose an enhanced …

Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
and it also provides valuable reference data for traffic control in advance. Three essential …

The Chinese pronunciation system offers two characteristics that distinguish it from other
languages: deep phonemic orthography and intonation variations. In this paper, we …