Chen Xia


Unsupervised Morphological Paradigm Completion
Huiming Jin | Liwei Cai | Yihui Peng | Chen Xia | Arya McCarthy | Katharina Kann
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems.


Visualizing Trends of Key Roles in News Articles
Chen Xia | Haoxiang Zhang | Jacob Moghtader | Allen Wu | Kai-Wei Chang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

There are tons of news generated every day reflecting the change of key roles such as people, organizations and political parties. Analyzing the trend of these key roles can help understand the information flow in a more effective way. In this paper, we present a demonstration system that visualizes the news trend of key roles based on natural language processing techniques. Specifically, we apply semantic role labelling to understand relationships between key roles in the news. We also train a dynamic word embedding model to align representations of words in different time periods to measure how the similarities between a key role and news topics change over time. Note: The github link to our demo jupyter notebook and screencast video is