When searching over the microblogging, users prefer using queries including terms that represent some specific entities. Meanwhile, tweets, though limited within 140 characters, are often generated with one or more entities. Entities, as an important part of tweets, usually convey rich information for modeling relevance from new perspectives. In this paper, we propose a feedback entity model and integrate it into an adaptive language modeling framework in order to improve the retrieval performance. The feedback entity model is estimated with the latest entity-associated tweets based upon a regularized maximum likelihood criterion. More specifically, we assume that the entity-associated tweets are generated by a mixture model, which consists of the entity model, the domain-specific language model and the collection language model. Experimental results on two public Text Retrieval Conference (TREC) Twitter corpora demonstrate the significant superiority of our approach over the state-of-the-art baselines.

Learning to rank method has been proposed for practical application in the field of information retrieval. When employing it in microblog retrieval, the significant interactions of various involved features are rarely considered. In this paper, we propose a Ranking Factorization Machine (Ranking FM) model, which applies Factorization Machine model to microblog ranking on basis of pairwise classification. In this way, our proposed model combines the generality of learning to rank framework with the advantages of factorization models in estimating interactions between features, leading to better retrieval performance. Moreover, three groups of features (content relevance features, semantic expansion features and quality features) and their interactions are utilized in the Ranking FM model with the methods of stochastic gradient descent and adaptive regularization for optimization. Experimental results demonstrate its superiority over several baseline systems on a real Twitter dataset in terms of P@30 and MAP metrics. Furthermore, it outperforms the best performing results in the TREC'12 Real-Time Search Task.

News comment is a new text genre which people use to express their opinions on recent news events. Different from normal text corpus, news comments have some particular properties. The named entities in the news comments usually use some wrongly written words, informal abbreviations or aliases, which bring great difficulties for machine detection and understanding. This paper addresses the issue of named entity resolution in Chinese news comments on the web, which is a special case of coreference resolution. Traditional resolution algorithms have some limitations for this special task. In this paper, we first define the special task, and then propose a novel resolution algorithm with new features to improve the resolution performance. We manually labeled a benchmark dataset with 60 pieces of news and their corresponding comments downloaded from a popular Chinese news portal and the experimental results on the dataset show that our algorithm is effective for this special task.

This poster briefly describes a practical system named FounderWISE for harvesting and monitoring hot topics on the Web. FounderWISE consists of five components: Web crawler, text classifier, topic detector, topic summarizer and topic analyzer. In this poster we present two key components of topic detector and topic analyzer. The system has been successfully deployed in a few Chinese major government departments.

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.

Almost all existing methods conduct the summarization tasks 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 framework called CollabSum for collaborative single document summarizations by making use of mutual influences of multiple documents within a cluster context. In this study, CollabSum is implemented by first employing the clustering algorithm to obtain appropriate document clusters and then exploiting the graph-ranking based algorithm for collaborative document summarizations within each cluster. Both the with-document and cross-document relationships between sentences are incorporated in the algorithm. Experiments on the DUC2001 and DUC2002 datasets demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the summarization performance relies positively on the quality of document cluster.

Many text documents on the Web are not originally created but forwarded or copied from other source documents. The phenomenon of document forwarding or transmission between various web sites is denoted as Web information diffusion. This paper focuses on mining information diffusion processes for specific topics on the Web. A novel system called LIDPW is proposed to address this problem using matching learning techniques. The source site and source document of each document are identified and the diffusion process composed of a sequence of diffusion relationships is visually presented to users. The effectiveness of LIDPW is validated on a real data set. A preliminary user study is performed and the results show that LIDPW does benefit users to monitor the information diffusion process of a specific topic, and aid them to discover the diffusion start and diffusion center of the topic.

Graph-ranking based methods have been developed for generic multi-document summarization in recent years and they make uniform use of the relationships between sentences to extract salient sentences. This paper proposes to integrate the relevance of the sentences to the specified topic into the graph-ranking based method for topic-focused multi-document summarization. The cross-document relationships and the within-document relationships between sentences are differentiated and we apply the graph-ranking based method using each individual kind of sentence relationships and explore their relative importance for topic-focused multi-document summarization. Experimental results on DUC2003 and DUC2005 demonstrate the great importance of the cross-document relationships between sentences for topic-focused multi-document summarization. Even the approach based only on the cross-document sentence relationships can perform better than or at least as well as the approaches based on both kinds of sentence relationships

This paper proposes a novel approach to measuring XML document similarity by taking into account the semantics between XML elements. The motivation of the proposed approach is to overcome the problems of "under-contributionö and "over-contributionö existing in previous work. The element semantics are learned in an unsupervised way and the Proportional Transportation Similarity is proposed to evaluate XML document similarity by modeling the similarity calculation as a transportation problem. Experiments of clustering are performed on three ACM SIGMOD data sets and results show the favorable performance of the proposed approach.

Structured link vector model (SLVM) is a recently proposed document representation that takes into account both structural and semantic information for measuring XML document similarity. Its formulation includes an element similarity matrix for capturing the semantic similarity between XML elements - the structural components of XML documents. In this paper, instead of applying heuristics to define the similarity matrix, we proposed to learn the matrix using pair wise similar training data in an iterative manner. In addition, we extended SLVM to SLVM-LSI by incorporating term semantics into SLVM using latent semantic indexing, with the element similarity related properties of the original SLVM preserved. For performance evaluation, we applied SLVM-LSI to similarity-based clustering of two XML datasets and the proposed SLVM-LSI was found to significantly outperform the conventional vector space model and the edit-distance based methods. The similarity matrix, obtained as a byproduct via the learning, can provide higher level knowledge about the semantic relationship between the XML elements.