Marjorie Freedman

Who is Who and What is What: Experiments in Cross-Document Co-Reference

This paper describes a language-independent, scalable system for both challenges of cross-document co-reference: name variation and entity disambiguation. We provide system results from the ACE 2008 evaluation in both English and Arabic. Our English systems accuracy is 8.4% relative better than an exact match baseline (and 14.2% relative better over entities mentioned in more than one document). Unlike previous evaluations, ACE 2008 evaluated both name variation and entity disambiguation over naturally occurring named mentions. An information extraction engine finds document entities in text. We describe how our architecture designed for the 10K document ACE task is scalable to an even larger corpus. Our cross-document approach uses the names of entities to find an initial set of document entities that could refer to the same real world entity and then uses an agglomerative clustering algorithm to disambiguate the potentially co-referent document entities. We analyze how different aspects of our system affect performance using ablation studies over the English evaluation set. In addition to evaluating cross-document co-reference performance, we used the results of the cross-document system to improve the accuracy of within-document extraction, and measured the impact in the ACE 2008 within-document evaluation.

Researching persons & organizations: AWAKE: From text to an entity-centric knowledge base

We describe a pilot experiment building a capability to automatically read documents, develop a knowledge base, support analytics, and visualize the information found. The capability allows someone researching a topic of interest of focus on analysis and synthesis rather than on reading. We show how information from multiple modalities (speech, text, structured databases) and multiple approaches (ontology driven and open information extraction) can be fused to create a resource about both previously known and novel entities. We describe an extensible framework for language understanding tools that allows for scalability, plug-and-play of alternative components, and incorporation of additional input streams, including video, images, and foreign language text.

Combining rule-based and statistical mechanisms for low-resource named entity recognition

We describe a multifaceted approach to named entity recognition that can be deployed with minimal data resources and a handful of hours of non-expert annotation. We describe how this approach was applied in the 2016 LoReHLT evaluation and demonstrate that both statistical and rule-based approaches contribute to our performance. We also demonstrate across many languages the value of selecting the sentences to be annotated when training on small amounts of data.