I am Rui Meng (孟睿), a 3rd year Ph.D. student at School of Computing and Information, University of Pittsburgh. I am advised by Prof. Daqing He and a member of Information Retrieval, Integration and Synthesis Lab. I also work with Prof. Peter Brusilovsky.
Currently my research focuses on Natural Language Processing and Machine Learning. Especially I am interested in Deep Learning for Semantic Representation and Information Extraction. Right now I am working on an exciting project regarding automatic keyphrase extraction with deep neural networks.
Before coming to Pittsburgh, I received my bachelor and master degree from Wuhan University (regarded as one of the most beautiful campuses and top universities in China), and I worked
with Prof. Wei Lu.
Have a look at my resume for more information.
显然物理世界对语言的影响不至于保存媒介，我感觉影响最大的一点是语言只能通过序列的形式存在。具体而言就是，时间导致语言只能按照从前到后的形式出现，因为人在同一时间只能发出一个声音，不可能以二维甚至多维的形式传递信息，比如通过嘴在空气中说出一幅画或者一个物体 :D 否则可能就会产生别的更好玩的语言形式了。这可能也是为什么RNN这种更关注序列的model比CNN对语言的建模能力更强（瞎说的，但欢迎拍砖）。
A Brief Review of Neural Network on Spoken Language Understanding
One project requires to do keyphrase extraction on scientific text. As most keyphrases appear in the text, so I am considering that whether this problem can be framed as a sequence labeling task , just like NER and POS-tagging.
Recently I come across a few papers about Neural Network applications on slot filling task, a subtask of spoken language understanding. Similarly, this task also can be addressed as a a standard sequence labeling task. So I hope I can get inspired somehow from their research, and the following is some notes about these papers. It’s worth noting that this posting doesn’t cover all the Neural Network research regarding the slot filling task, mostly from MSR.
1. Task Introduction: Slot Filling and Recurrent Neural Network
A little bit of description of the slot filling task as well as the data would help you understand what’s going on here. The figure below shows an example in ATIS dataset, with the annotation of slot/concept, named entity, intent as well as domain. The latter two annotations are for the other two tasks in SLU: domain detection and intent determination. We can see that the slot filling is quite similar to the NER task, following the IOB tagging representation, except for a more specific granularity.
An example of IOB representation for ATIS dataset
So our task is to translate the original sentence into the IOB tagging form. Following the RNN structures shown below, the model structure we used for sequence labeling should be the last one, which outputs a label for each input word. There are many awesome articles can offer you intuitive understanding and techniques about RNN, so I won’t refer to much detail here.
Some common architectures of recurrent neural networks
2. Paper Notes
2.1 Investigation of Recurrent-Neural-Network Architectures and Learning Methods for Spoken Language Understanding