Neural Cross-Lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a low-resource language.Comment: EMNLP 2018 long pape

China’s Miracle: From the Perspective of Political Economy

We construct a framework for the interaction between economic system reform and the technological-economic system in order to analyze the dynamic process of China’s economic transformation and development. China has passed through three major phases of economic system reform, which have involved reforming the commodity economy through planning, establishing the socialist market economic system, and improving the socialist market economic system. Correspondingly, China has gone through three technological-economic systems, which may be summed up as: “quantitative subsistence consumption and extensive production without technological progress,” “qualitative subsistence consumption and extensive production with technological progress,” and “standardized mass consumption and mass production.” Since 2012, China’s economy has entered the era of a “new normal,” characterized by lower growth rates. This indicates a fundamental shift in the patterns of social demand away from the current technological-economic system that is growing incapable of sustaining rapid capital accumulation and thus needs transforming. To better explain China’s miracle, we focus on the ways in which the contradictions within each technological-economic system have evolved and have been resolved through targeted reforms to the economic system. Eventually, these reforms will lead to a new system that facilitates further capital accumulation

Integrating Chinese Data from Sina Weibo to the LITMUS Landslide Detection System

The detection of landslides has been a challenging problem for researchers since there are no dedicated physical sensors to detect landslides. LITMUS is a landslide detection system based on information from both social media platforms and physical sensors. It does have its own limitations, however, because it only supports English data. We propose to integrate the Chinese data from Sina Weibo to the LITMUS landslide detection system to extend its service. The Chinese LITMUS system pipeline starts off by collecting data from Sina Weibo using a web crawler. Then, it applies a few filtering techniques to tackle part of the noise that comes with the dataset. Subsequently, the system uses a combination of Named Entity Recognition (NER)-based and gazetteer-based approach to geo-tag the data items. The data-items that contain the same location entity are grouped to one cluster, which represents a candidate event. The system then classifies each data item to identify the remaining noise by using Word2Vec and Support Vector Machine (SVM). Lastly, the system makes a decision based on the majority label assigned to each cluster by the classifier as to whether or not a candidate event is an actual landslide event. Through our experiments, we show that the classification component of the system achieves about 0.96 in precision, recall and F-measure using the evaluation dataset, and that the system is able to detect a large number of landslides in China.Undergraduat