Loo-Nin Teow

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Named entity recognition (NER) is the task of segmenting and classifying occurrences of names in text. In NER, local contextual cues provide important evidence, but non-local information from the whole document could also prove useful: for example, it is useful to know that “Mary Kay Inc.” has been mentioned in a document to classify(More)
Centrality measures are crucial in quantifying the roles and positions of vertices in networks. An important measure is betweenness, which is based on the number of shortest paths that vertices fall on. However, betweenness is computationally expensive to derive, resulting in much research on efficient techniques. We note that in many applications, the key(More)
We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results(More)
This paper seeks to investigate how the existing knowledge in a cognitive system can be used to help fill-in an incomplete situation picture. This is motivated by the human brain's innate ability to use new incoming information together with stored knowledge to fill in gaps of the whole picture in the mind. The research focus of this paper is on the(More)
We use well-established results in biological vision to construct a novel vision model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear classifier on these features, our model is relatively simple yet outperforms other models on the(More)
Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile(More)
A troll is a user intent on sowing discord on the internet. We propose an approach to detect such users from the sentiment of the textual content in online forums. Since trolls typically express negative sentiments in their posts, we derive features from sentiment analysis, and use SVM<sup>rank</sup> to do binary and ordinal classification of trolls. With a(More)