[7-31] Learning Representation for Fine-Grained Text Analysis |
时间:2015-07-28 |
SKLCS Seminar Title: Learning Representation for Fine-Grained Text Analysis Speaker: Lizhen Qu (Macquarie University, Australia) people.mpi-inf.mpg.de/~lqu Time: 31st July 2015, 15:00 Venue: Seminar Room (334), Level 3, Building 5, Institute of Software, Chinese Academy of Sciences (CAS), 4 Zhongguancun South Fourth Street, Haidian District, Beijing 100190 Abstract: My talk will consist of two parts. In the first part of the talk I will present Senti-LSSVM model for sentiment-oriented relation extraction. This task aims to jointly extract both sentiments (e.g. Paul likes Nexus 5.) and comparisons (e.g. Paul thinks Nexus 5 is better than Galaxy S5.) from sentences. The corresponding outputs are directed hyper-graphs and the lexical features are learned with recursive neural networks. In the second part, I will introduce my recent work at NICTA on applying deep learning techniques to a number of natural language processing tasks, including identification of multi-word expressions, named entity recognition, part-of-speech tagging, and chunking. We find that deep learning techniques perform especially well on cross-domain tasks. We have achieved 10% improvement over competitive baselines on named entity recognition for novel types. |