Guoqing Zheng

Learning to Reweight Terms with Distributed Representations

Term weighting is a fundamental problem in IR research and numerous weighting models have been proposed. Proper term weighting can greatly improve retrieval accuracies, which essentially involves two types of query understanding: interpreting the query and judging the relative contribution of the terms to the query. These two steps are often dealt with separately, and complicated yet not so effective weighting strategies are proposed. In this paper, we propose to address query interpretation and term weighting in a unified framework built upon distributed representations of words from recent advances in neural network language modeling. Specifically, we represent term and query as vectors in the same latent space, construct features for terms using their word vectors and learn a model to map the features onto the defined target term weights. The proposed method is simple yet effective. Experiments using four collections and two retrieval models demonstrates significantly higher retrieval accuracies than baseline models.

News comments generation via mining microblogs

Microblogging websites such as Twitter and Chinese Sina Weibo contain large amounts of microblogs posted by users. Many of these microblogs are highly sensitive to the important real-world events and correlated to the news events. Thus, microblogs from these websites can be collected as comments for the news to reveal the opinions and attitude towards the news event among large number of users. In this paper, we present a framework to automatically collect relevant microblogs from microblogging websites to generate comments for popular news on news websites.

Probabilistic text modeling with orthogonalized topics

Topic models have been widely used for text analysis. Previous topic models have enjoyed great success in mining the latent topic structure of text documents. With many efforts made on endowing the resulting document-topic distributions with different motivations, however, none of these models have paid any attention on the resulting topic-word distributions.Since topic-word distribution also plays an important role in the modeling performance,topic models which emphasize only the resulting document-topic representations but pay less attention to the topic-term distributions are limited. In this paper, we propose the Orthogonalized Topic Model(OTM) which imposes an orthogonality constraint on the topic-term distributions. We also propose a novel model fitting algorithm based on the generalized Expectation-Maximization algorithm and the Newthon-Raphson method. Quantitative evaluation of text classification demonstrates that OTM outperforms other baseline models and indicates the important role played by topic orthogonalizing.

A topical link model for community discovery in textual interaction graph

This paper is concerned with community discovery in textual interaction graph, where the links between entities are indicated by textual documents. Specifically, we propose a Topical Link Model(TLM), which leverages Hierarchical Dirichlet Process(HDP) to introduce hidden topical variable of the links. Other than the use of links, TLM can look into the documents on the links in detail to recover sound communities. Moreover, TLM is a nonparametric model, which is able to learn the number of communities from the data. Extensive experiments on two real world corpora show TLM outperforms two state-of-the-art baseline models, which verify the effectiveness of TLM in determining the proper number of communities and generating sound communities.

Efficient Shift-Invariant Dictionary Learning

Shift-invariant dictionary learning (SIDL) refers to the problem of discovering a set of latent basis vectors (the dictionary) that captures informative local patterns at different locations of the input sequences, and a sparse coding for each sequence as a linear combination of the latent basis elements. It differs from conventional dictionary learning and sparse coding where the latent basis has the same dimension as the input vectors, where the focus is on global patterns instead of shift-invariant local patterns. Unsupervised discovery of shift-invariant dictionary and the corresponding sparse coding has been an open challenge as the number of candidate local patterns is extremely large, and the number of possible linear combinations of such local patterns is even more so. In this paper we propose a new framework for unsupervised discovery of both the shift-invariant basis and the sparse coding of input data, with efficient algorithms for tractable optimization. Empirical evaluations on multiple time series data sets demonstrate the effectiveness and efficiency of the proposed method.

Experiments in Curation: Towards Machine-Assisted Construction of Software Architecture Knowledge Bases

Software architects inhabit a complex, rapidly evolving technological landscape. An ever growing collection of competing architecturally significant technologies, ranging from distributed databases to middleware and cloud platforms, makes rigorously comparing alternatives and selecting appropriate solutions a daunting engineering task. To address this problem, we envisage an ecosystem of curated, automatically updated knowledge bases that enable straightforward and streamlined technical comparisons of related products. These knowledge bases would emulate engineering handbooks that are commonly found in other engineering disciplines. As a first step towards this vision, we have built a curated knowledge base for comparing distributed databases based on a semantically defined feature taxonomy. We report in this paper on the initial results of using supervised machine learning to assist with knowledge base curation. Our results show immense promise in recommending Web pages that are highly relevant to curators. We also describe the major obstacles, both practical and scientific, that our work has uncovered. These must be overcome by future research in order to make our vision of curated knowledge bases a reality.