Xiaojun Wan

Co-Training for Cross-Lingual Sentiment Classification

The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine translation services are used for eliminating the language gap between the training set and test set, and English features and Chinese features are considered as two independent views of the classification problem. We propose a cotraining approach to making use of unlabeled Chinese data. Experimental results show the effectiveness of the proposed approach, which can outperform the standard inductive classifiers and the transductive classifiers.

Multi-document summarization using cluster-based link analysis

The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the sentences are indistinguishable from each other. However, a given document set usually covers a few topic themes with each theme represented by a cluster of sentences. The topic themes are usually not equally important and the sentences in an important theme cluster are deemed more salient than the sentences in a trivial theme cluster. This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information. Experimental results on the DUC2001 and DUC2002 datasets demonstrate the good effectiveness of our proposed summarization models. The results also demonstrate that the ClusterCMRW model is more robust than the ClusterHITS model, with respect to different cluster numbers.

Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis

It is a challenging task to identify sentiment polarity of Chinese reviews because the resources for Chinese sentiment analysis are limited. Instead of leveraging only monolingual Chinese knowledge, this study proposes a novel approach to leverage reliable English resources to improve Chinese sentiment analysis. Rather than simply projecting English resources onto Chinese resources, our approach first translates Chinese reviews into English reviews by machine translation services, and then identifies the sentiment polarity of English reviews by directly leveraging English resources. Furthermore, our approach performs sentiment analysis for both Chinese reviews and English reviews, and then uses ensemble methods to combine the individual analysis results. Experimental results on a dataset of 886 Chinese product reviews demonstrate the effectiveness of the proposed approach. The individual analysis of the translated English reviews outperforms the individual analysis of the original Chinese reviews, and the combination of the individual analysis results further improves the performance.

Evolutionary timeline summarization: a balanced optimization framework via iterative substitution

Classic news summarization plays an important role with the exponential document growth on the Web. Many approaches are proposed to generate summaries but seldom simultaneously consider evolutionary characteristics of news plus to traditional summary elements. Therefore, we present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Given the massive collection of time-stamped web documents related to a general news query, ETS aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity. ETS greatly facilitates fast news browsing and knowledge comprehension and hence is a necessity. We formally formulate the task as an optimization problem via iterative substitution from a set of sentences to a subset of sentences that satisfies the above requirements, balancing coherence/diversity measurement and local/global summary quality. The optimized substitution is iteratively conducted by incorporating several constraints until convergence. We develop experimental systems to evaluate on 6 instinctively different datasets which amount to 10251 documents. Performance comparisons between different system-generated timelines and manually created ones by human editors demonstrate the effectiveness of our proposed framework in terms of ROUGE metrics.

A novel document similarity measure based on earth mover's distance

In this paper we propose a novel measure based on the earth movers distance (EMD) to evaluate document similarity by allowing many-to-many matching between subtopics. First, each document is decomposed into a set of subtopics, and then the EMD is employed to evaluate the similarity between two sets of subtopics for two documents by solving the transportation problem. The proposed measure is an improvement of the previous OM-based measure, which allows only one-to-one matching between subtopics. Experiments have been performed on the TDT3 dataset to evaluate existing similarity measures and the results show that the EMD-based measure outperforms the optimal matching (OM) based measure and all other measures. In addition to the TextTiling algorithm, the sentence clustering algorithm is adopted for document decomposition, and the experimental results show that the proposed EMD-based measure does not rely on the document decomposition algorithm and thus it is more robust than the OM-based measure.

CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase Extraction

Previous methods usually conduct the keyphrase extraction task for single documents separately without interactions for each document, under the assumption that the documents are considered independent of each other. This paper proposes a novel approach named CollabRank to collaborative single-document keyphrase extraction by making use of mutual influences of multiple documents within a cluster context. CollabRank is implemented by first employing the clustering algorithm to obtain appropriate document clusters, and then using the graph-based ranking algorithm for collaborative single-document keyphrase extraction within each cluster. Experimental results demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the system performance relies positively on the quality of document clusters.

Exploiting neighborhood knowledge for single document summarization and keyphrase extraction

Document summarization and keyphrase extraction are two related tasks in the IR and NLP fields, and both of them aim at extracting condensed representations from a single text document. Existing methods for single document summarization and keyphrase extraction usually make use of only the information contained in the specified document. This article proposes using a small number of nearest neighbor documents to improve document summarization and keyphrase extraction for the specified document, under the assumption that the neighbor documents could provide additional knowledge and more clues. The specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results on the Document Understanding Conference (DUC) benchmark datasets demonstrate the effectiveness and robustness of our proposed approaches. The cross-document sentence relationships in the expanded document set are validated to be beneficial to single document summarization, and the word cooccurrence relationships in the neighbor documents are validated to be very helpful to single document keyphrase extraction.

Improved Affinity Graph Based Multi-Document Summarization

This paper describes an affinity graph based approach to multi-document summarization. We incorporate a diffusion process to acquire semantic relationships between sentences, and then compute information richness of sentences by a graph rank algorithm on differentiated intra-document links and inter-document links between sentences. A greedy algorithm is employed to impose diversity penalty on sentences and the sentences with both high information richness and high information novelty are chosen into the summary. Experimental results on task 2 of DUC 2002 and task 2 of DUC 2004 demonstrate that the proposed approach outperforms existing state-of-the-art systems.

An Exploration of Document Impact on Graph-Based Multi-Document Summarization

The graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relationships in the documents, under the assumption that all the sentences are indistinguishable. However, given a document set to be summarized, different documents are usually not equally important, and moreover, different sentences in a specific document are usually differently important. This paper aims to explore document impact on summarization performance. We propose a document-based graph model to incorporate the document-level information and the sentence-to-document relationship into the graph-based ranking process. Various methods are employed to evaluate the two factors. Experimental results on the DUC2001 and DUC2002 datasets demonstrate that the good effectiveness of the proposed model. Moreover, the results show the robustness of the proposed model.

Using only cross-document relationships for both generic and topic-focused multi-document summarizations

In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the “votings” or “recommendations” between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002–DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.