Xiaojun Chen


Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text
Shengbin Jia | Ling Ding | Xiaojun Chen | Shijia E | Yang Xiang
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems. However, segmentation error propagation is a challenge for Chinese NER while processing colloquial data like social media text. In this paper, we propose a model (UIcwsNN) that specializes in identifying entities from Chinese social media text, especially by leveraging uncertain information of word segmentation. Such ambiguous information contains all the potential segmentation states of a sentence that provides a channel for the model to infer deep word-level characteristics. We propose a trilogy (i.e., Candidate Position Embedding => Position Selective Attention => Adaptive Word Convolution) to encode uncertain word segmentation information and acquire appropriate word-level representation. Experimental results on the social media corpus show that our model alleviates the segmentation error cascading trouble effectively, and achieves a significant performance improvement of 2% over previous state-of-the-art methods.


Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters
Min Yang | Jincheng Mei | Heng Ji | Wei Zhao | Zhou Zhao | Xiaojun Chen
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information. The experimental results demonstrate that LDST performs very well at discovering topics and sentiments from social media and tracking their shifts in different geographical regions during emergencies and disasters. We will release the data and source code after this work is published.