Bridging the lexical gap between the user’s question and the question-answer pairs in the Q&A archives has been a major challenge for Q&A retrieval. State-of-the-art approaches address this issue by implicitly expanding the queries with additional words using statistical translation models. While useful, the effectiveness of these models is highly dependant on the availability of quality corpus...

It has long been recognized that the concept of inconsistency is a central part of commonsense reasoning. In this issue, a number of authors have explored the idea of reasoning with maximal consistent subsets of an inconsistent stratified knowledge base. This paradigm, often called “coherent-based reasoning", has resulted in some interesting proposals for paraconsistent reasoning, non-monotonic...

Entity Analysis with Weak Supervision: Typing, Linking, and Attribute Extraction Xiao Ling Chair of the Supervisory Committee: Professor Daniel S. Weld Computer Science and Engineering With the advent of the Web, textual information has grown at an explosive rate. To digest this enormous amount of data, an automatic solution, Information Extraction (IE), has become necessary. Information extrac...

Entity Linking (EL) is the task of detecting mentions entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that base complete. In practice, however, it necessary deal with case linking an entity not contained (NIL entity). Recent works have shown that, instead focusing only on affinities between entities, considering inter-mention can be us...

Nowadays, most deep learning models ignore Chinese habits and global information when processing tasks. To solve this problem, we constructed the BERT-BiLSTM-Attention-CRF model. In model, embeded BERT pre-training language model that adopts Whole Word Mask strategy, added a document-level attention. Experimental results show our method achieves good in MSRA corpus, F1 reaches 95.00%.