On October 4th, Jake Lever, a PHD candidate in Bioinformatics from UBC will give a talk about his research about knowledge discovery during the lab meeting. He is going to talk about three of his major projects. The first one focuses on knowledge discovery by inferring relations between biomedical concepts using approaches from recommender systems; the second focuses on biomedical relation extraction and this year’s BioNLP shared task; and the third talks about using NLP to build knowledge bases for personalised medicine.
On September 20th 2:30pm, during the lab meeting, PHD student Ashkan Alinejad will give a presentation on his master thesis about speech recognition using recurrent neural network solutions.
On July 20th 2016, Zhelun Wu gave his master thesis defence on the topic of “Decipherment of Evasive or Encrypted Offensive Text”.
Here is the Abstract: A very common computational task in monitoring online chat sessions is stopping users from sending malicious chat messages. Examples of malicious messages include age-inappropriate language, cyber-bullying, and sending out personal information. Rule based filtering systems are commonly used to deal with this problem, but not all of the malicious messages can be filtered out as people invent increasingly subtle ways to disguise their malicious messages to bypass such filtering systems. Machine learning classifiers can also be used to identify and filter malicious messages. However, such classifiers still rely on training data that becomes out of date and new forms of malicious text cannot be detected by the classifier. In this thesis, to solve this problem we model the messages corrupted by a malicious user to bypass a chat filter as a cipher text. We apply automatic decipherment techniques using Expectation-Maximization with Hidden Markov Models and a beam search algorithm in order to decrypt corrupted malicious text back into plain text which can be then filtered using rules or a classifier.
At the June 22nd lab meeting (11:30am), Anahita will talk about Joint Prediction of Word Alignment with Alignment Types.
Abstract: Probabilistic models for word alignment do not distinguish between different types of word alignment links. We provide a new probabilistic model for word alignment in which alignments are associated with linguistically motivated alignment types. We provide a novel task of joint prediction of word alignment and alignment types.
On May 25th 11:30am, Andrei will give a presentation on the CKY algorithm and Canny, Hall, and Klein’s 2013 paper “A Multi-Teraflop Constituency Parser using GPUs.”