Chandra Bhagavatula

Semi-supervised sequence tagging with bidirectional language models

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pre- trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.

TabEL: Entity Linking in Web Tables

Web tables form a valuable source of relational data. The Web contains an estimated 154 million HTML tables of relational data, with Wikipedia alone containing 1.6 million high-quality tables. Extracting the semantics of Web tables to produce machine-understandable knowledge has become an active area of research. A key step in extracting the semantics of Web content is entity linking EL: the task of mapping a phrase in text to its referent entity in a knowledge base KB. In this paper we present TabEL, a new EL system for Web tables. TabEL differs from previous work by weakening the assumption that the semantics of a table can be mapped to pre-defined types and relations found in the target KB. Instead, TabEL enforces soft constraints in the form of a graphical model that assigns higher likelihood to sets of entities that tend to co-occur in Wikipedia documents and tables. In experiments, TabEL significantly reduces error when compared to current state-of-the-art table EL systems, including a

WebSAIL wikifier at ERD 2014

75\%

Using natural language to integrate, evaluate, and optimize extracted knowledge bases

error reduction on Wikipedia tables and a

CONTENT-BASED CITATION RECOMMENDATION

60\%

WebSAIL Wikifier: English Entity Linking at TAC 2013

error reduction on Web tables. We also make our parsed Wikipedia table corpus and test datasets publicly available for future work.