Information Extraction for New Emerging Noisy User-generated Micro-Text

Neural networks have proved highly effective at extracting information from text. However, noisy microtext has proved to be particularly difficult because low-level syntactic cues much less useful. In this project, we propose to explore ways of incorporating strong semantic, expectation-based models into a neural net architecture to improve performance on microtext extraction. In phase I, we will consider a strong semantic model that uses an massive knowledge graph to generate interest profiles from a user’s tweet history, and investigate several ideas for incorporating that type of contextual model into a modern neural net architecture. Our approach, if successful, will not only improve the extraction accuracy on noisy microtext, but will also address one of the major problems with neural networks in dynamic domains where new entities emerge frequently. Neural models are trained on a frozen “snapshot” of the world, and the stored information can only be updated through potentially costly retraining or fine tuning. Our work, if successful, will allow systems to be updated much more rapidly and effectively when the world changes.