Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER

BiLSTM has been prevalently used as a core module for NER in a sequence-labeling setup. State-of-the-art approaches use BiLSTM with additional resources such as gazetteers, language-modeling, or multi-task supervision to further improve NER. This paper instead takes a step back and focuses on analyzing problems of BiLSTM itself and how exactly self-attention can bring improvements. We formally show the limitation of (CRF-)BiLSTM in modeling cross-context patterns for each word -- the XOR limitation. Then, we show that two types of simple cross-structures -- self-attention and Cross-BiLSTM -- can effectively remedy the problem. We test the practical impacts of the deficiency on real-world NER datasets, OntoNotes 5.0 and WNUT 2017, with clear and consistent improvements over the baseline, up to 8.7% on some of the multi-token entity mentions. We give in-depth analyses of the improvements across several aspects of NER, especially the identification of multi-token mentions. This study should lay a sound foundation for future improvements on sequence-labeling NER. (Source codes: https://github.com/jacobvsdanniel/cross-ner)Comment: In proceedings of AAAI 202

Named entity recognition for sensitive data discovery in Portuguese

The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, two statistical models were tested—Conditional Random Fields and Random Forest and, finally, a Bidirectional-LSTM approach as experimented. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus, and DataSense NER Corpus.info:eu-repo/semantics/publishedVersio

Writing for Local Government Schools: Authors and Themes in Song-dynasty School Inscriptions

A hallmark of the Song dynasty\u27s achievements was the creation of a national network of state-sponsored local schools. This engendered an exponential growth of commemorative inscriptions dedicated to local government schools. Many authors used these inscriptions as an avenue to expound and disseminate their visions of schools and education. Using the methods of network analysis and document clustering, this article analyzes all the inscriptions extant from Song times for local government schools. It reveals a structural schism in the diffusion of ideas between the Upper Yangzi and other regions of the Song. It also demonstrates the growing intellectual influence of Neo-Confucian ideologues that gradually overtook that of renowned prose-writers. Methodologically, this article provides an example of how diverse digital methods enable us to handle a large body of texts from multiple perspectives and invite us to explore connections we might not have otherwise thought of. Free access link: https://www.cambridge.org/core/journals/journal-of-chinese-history/article/writing-for-local-government-schools-authors-and-themes-in-songdynasty-school-inscriptions/8917993FA5EC53FC837961E6B929856F/share/eb301b0b72c9781fb464765a830a50b029453e6