CSE Colloquium: Learning Structured Information from Language

Abstract: Extracting information from text entails deriving a structured, and typically domain-specific, representation of entities and relations from unstructured text. The information thus extracted can potentially facilitate applications such as question answering, information retrieval, conversational dialogue and opinion analysis. However, extracting information from text in a structured form is difficult: it requires understanding words and the relations that exist between them in the context of both the current sentence and the document as a whole.

In this talk, I will present my research on neural models that learn structured output representations comprised of textual mentions of entities and relations within a sentence.
In particular, I will propose the use of novel output representations that allow the neural models to learn better dependencies in the output structure and achieve state-of-the-art performance on both tasks as well as on nested variations. I will also describe our recent
work on expanding the input context beyond sentences by incorporating coreference resolution to learn entity-level rather than mention-level representations and show that these representations can capture the information regarding the saliency of entities in the document.

Biography: Arzoo Katiyar is a PhD candidate in Computer Science at Cornell University, where she is advised by Claire Cardie. Her recent interests include natural language processing and machine learning. In particular, she is interested in developing neural network models for structured prediction problems in natural language processing for information extraction. Previously, she received her BTech-MTech degree in Computer Science and Engineering from IIT Kanpur.

 

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Media Contact: Rebecca Passonneau

 
 

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