Author name cluster

Lan Yi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2019 Conference Paper

Adaptive Region Embedding for Text Classification

Liuyu Xiang
Xiaoming Jin
Lan Yi
Guiguang Ding

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state-of-the-art performances and effectively avoids word ambiguity.

PDF Details

IJCAI Conference 2018 Conference Paper

Automatic Gating of Attributes in Deep Structure

Xiaoming Jin
Tao He
Cheng Wan
Lan Yi
Guiguang Ding
Dou Shen

Deep structure has been widely applied in a large variety of fields for its excellence of representing data. Attributes are a unique type of data descriptions that have been successfully utilized in numerous tasks to enhance performance. However, to introduce attributes into deep structure is complicated and challenging, because different layers in deep structure accommodate features of different abstraction levels, while different attributes may naturally represent the data in different abstraction levels. This demands adaptively and jointly modeling of attributes and deep structure by carefully examining their relationship. Different from existing works that treat attributes straightforwardly as the same level without considering their abstraction levels, we can make better use of attributes in deep structure by properly connecting them. In this paper, we move forward along this new direction by proposing a deep structure named Attribute Gated Deep Belief Network (AG-DBN) that includes a tunable attribute-layer gating mechanism and automatically learns the best way of connecting attributes to appropriate hidden layers. Experimental results on a manually-labeled subset of ImageNet, a-Yahoo and a-Pascal data set justify the superiority of AG-DBN against several baselines including CNN model and other AG-DBN variants. Specifically, it outperforms the CNN model, VGG19, by significantly reducing the classification error from 26. 70% to 13. 56% on a-Pascal.

PDF Details

IJCAI Conference 2003 Conference Paper

Web Page Cleaning for Web Mining through Feature Weighting

Lan Yi
Bing Liu

Unlike conventional data or text, Web pages typically contain a large amount of information that is not part of the main contents of the pages, e. g. , banner ads, navigation bars, and copyright notices. Such irrelevant information (which we call Web page noise) in Web pages can seriously harm Web mining, e. g. , clustering and classification. In this paper, we propose a novel feature weighting technique to deal with Web page noise to enhance Web mining. This method first builds a compressed structure tree to capture the common structure and comparable blocks in a set of Web pages. It then uses an information based measure to evaluate the importance of each node in the compressed structure tree. Based on the tree and its node importance values, our method assigns a weight to each word feature in its content block. The resulting weights are used in Web mining. We evaluated the proposed technique with two Web mining tasks, Web page clustering and Web page classification. Experimental results show that our weighting method is able to dramatically improve the mining results.

PDF