Modeling the process of information diffusion is a challenging problem. Although numerous attempts have been made in order to solve this problem, very few studies are actually able to simulate and predict temporal dynamics of the diffusion process. In this paper, we propose a novel information diffusion model, namely GT model, which treats the nodes of a network as intelligent and rational agents and then calculates their corresponding payoffs, given different choices to make strategic decisions. By introducing time-related payoffs based on the diffusion data, the proposed GT model can be used to predict whether or not the user's behaviors will occur in a specific time interval. The user's payoff can be divided into two parts: social payoff from the user's social contacts and preference payoff from the user's idiosyncratic preference. We here exploit the global influence of the user and the social influence between any two users to accurately calculate the social payoff. In addition, we develop a new method of presenting social influence that can fully capture the temporal dynamics of social influence. Experimental results from two different datasets, Sina Weibo and Flickr demonstrate the rationality and effectiveness of the proposed prediction method with different evaluation metrics.

This paper proposes a novel hierarchical recurrent neural network language model (HRNNLM) for document modeling.After establishing a RNN to capture the coherence between sentences in a document, HRNNLM integrates it as the sentence history information into the word level RNN to predict the word sequence with cross-sentence contextual information.A two-step training approach is designed, in which sentence-level and word-level language models are approximated for the convergence in a pipeline style.Examined by the standard sentence reordering scenario, HRNNLM is proved for its better accuracy in modeling the sentence coherence.And at the word level, experimental results also indicate a significant lower model perplexity, followed by a practical better translation result when applied to a Chinese-English document translation reranking task.

This paper introduces a maximum entropy method to Discourse Coherence Modeling (DCM). Different from the state-of-art supervised entity-grid model and unsupervised cohesion-driven model, the model we proposed only takes as input lexicon features, which increases the training speed and decoding speed significantly. We conduct an evaluation on two publicly available benchmark data sets via sentence ordering tasks, and the results confirm the effectiveness of our maximum entropy based approach in DCM.

How to model the process of information diffusion in social networks is a critical research task. Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process. To address this problem, we propose a novel information diffusion model (GT model), which considers the users in network as intelligent agents. The agent jointly considers all his interacting neighbors and calculates the payoffs for his different choices to make strategic decision. We introduce the time factor into the user payoff, enabling the GT model to not only predict the behavior of a user but also to predict when he will perform the behavior. Both the global influence and social influence are explored in the time-dependent payoff calculation, where a new social influence representation method is designed to fully capture the temporal dynamic properties of social influence between users. Experimental results on Sina Weibo and Flickr validate the effectiveness of our methods.

Linking name mentions in microblog posts to a knowledge base, namely microblog entity linking, is useful for text mining tasks on microblog.Entity linking in long text has been well studied in previous works.However few work has focused on short text such as microblog post.Microblog posts are short and noisy.Previous method can extract few features from the post context.In this paper we propose to use extra posts for the microblog entity linking task.Experimental results show that our proposed method significantly improves the linking accuracy over traditional methods by 8.3% and 7.5% respectively.

Conventional search engines usually return a ranked list of web pages in response to a query. Users have to visit several pages to locate the relevant parts. A promising future search scenario should involve: (1) understanding user intents; (2) providing relevant information directly to satisfy searchers' needs, as opposed to relevant pages. In this paper, we present a search paradigm to summarize a query's information from different aspects. Query aspects could be aligned to user intents. The generated summaries for query aspects are expected to be both specific and informative, so that users can easily and quickly find relevant information. Specifically, we use a Composite Query for Summarization" method, where a set of component queries are used for providing additional information for the original query. The system leverages the search engine to proactively gather information by submitting multiple component queries according to the original query and its aspects. In this way, we could get more relevant information for each query aspect and roughly classify information. By comparative mining the search results of different component queries, it is able to identify query (dependent) aspect words, which help to generate more specific and informative summaries. The experimental results on two data sets, Wikipedia and TREC ClueWeb2009, are encouraging. Our method outperforms two baseline methods on generating informative summaries.

In this paper, we propose a method to rank and assign weights to query terms according to their impact on the topic of the query. We use Search Result Overlap Ratio (SROR) to quantify the overlap of the search results of the full query and a shorten query after removing one term. Intuitively, if the overlap is small, it indicates a big topic shift and the removed term should be discriminative and important. The SROR could be used for measuring query term importance with a search engine automatically. By this way, learning based models could be trained based on a large number of automatically labeled instances and make predictions for future queries efficiently.

Statistical bilingual word alignment has been well studied in the field of machine translation. This article adapts the bilingual word alignment algorithm into a monolingual scenario to extract collocations from monolingual corpus, based on the fact that the words in a collocation tend to co-occur in similar contexts as in bilingual word alignment. First, the monolingual corpus is replicated to generate a parallel corpus, in which each sentence pair consists of two identical sentences. Next, the monolingual word alignment algorithm is employed to align potentially collocated words. Finally, the aligned word pairs are ranked according to the alignment scores and candidates with higher scores are extracted as collocations. We conducted experiments on Chinese and English corpora respectively. Compared to previous approaches that use association measures to extract collocations from co-occurrence word pairs within a given window, our method achieves higher precision and recall. According to human evaluation, our method achieves precisions of 62% on a Chinese corpus and 64% on an English corpus. In particular, we can extract collocations with longer spans, achieving a higher precision of 83% on the long-span (> 6 words) Chinese collocations.

The goal of predicting query potential for personalization is to determine which queries can benefit from personalization. In this paper, we investigate which kind of strategy is better for this task: classification or regression. We quantify the potential benefits of personalizing search results using two implicit click-based measures: Click entropy and Potential@N. Meanwhile, queries are characterized by query features and history features. Then we build C-SVM classification model and epsilon-SVM regression model respectively according to these two measures. The experimental results show that the classification model is a better choice for predicting query potential for personalization.

Logistic average misclassification percentage (lam%) is a key measure for the spam filtering performance. This paper demonstrates that a spam filter can achieve a perfect 0.00% in lam%, the minimal value in theory, by simply setting a biased threshold during the classifier modeling. At the same time, the overall classification performance reaches only a low accuracy. The result suggests that the role of lam% for spam filtering evaluation should be re-examined.

Abstract Paraphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented method, we extract more than 1 million pairs of paraphrase patterns from about 2 million pairs of bilingual parallel sentences. The precision of the extracted paraphrase patterns is above 78%. Experimental results show that the presented method significantly outperforms a well-known method called discovery of inference rules from text (DIRT). Additionally, the log-linear model with the proposed feature functions are effective. The extracted paraphrase patterns are fully analyzed. Especially, we found that the extracted paraphrase patterns can be classified into five types, which are useful in multiple natural language processing (NLP) applications.

As a kind of Shallow Semantic Parsing, Semantic Role Labeling (SRL) is gaining more attention as it benefits a wide range of natural language processing applications. Given a sentence, the task of SRL is to recognize semantic arguments (roles) for each predicate (target verb or noun). Feature-based methods have achieved much success in SRL and are regarded as the state-of-the-art methods for SRL. However, these methods are less effective in modeling structured features. As an extension of feature-based methods, kernel-based methods are able to capture structured features more efficiently in a much higher dimension. Application of kernel methods to SRL has been achieved by selecting the tree portion of a predicate and one of its arguments as feature space, which is named as predicate-argument feature (PAF) kernel. The PAF kernel captures the syntactic tree structure features using convolution tree kernel, however, it does not distinguish between the path structure and the constituent structure. In this article, a hybrid convolution tree kernel is proposed to model different linguistic objects. The hybrid convolution tree kernel consists of two individual convolution tree kernels. They are a Path kernel, which captures predicate-argument link features, and a Constituent Structure kernel, which captures the syntactic structure features of arguments. Evaluations on the data sets of the CoNLL-2005 SRL shared task and the Chinese PropBank (CPB) show that our proposed hybrid convolution tree kernel statistically significantly outperforms the previous tree kernels. Moreover, in order to maximize the system performance, we present a composite kernel through combining our hybrid convolution tree kernel method with a feature-based method extended by the polynomial kernel. The experimental results show that the composite kernel achieves better performance than each of the individual methods and outperforms the best reported system on the CoNLL-2005 corpus when only one syntactic parser is used and on the CPB corpus when automated syntactic parse results and correct syntactic parse results are used respectively.

Convolution tree kernel has shown promising results in semantic role labeling (SRL). However, this kernel does not consider much linguistic knowledge in kernel design and only performs hard matching between subtrees. To overcome these constraints, this paper proposes a grammar-driven convolution tree kernel for SRL by introducing more linguistic knowledge. Compared with the standard convolution tree kernel, the proposed grammar-driven kernel has two advantages: 1) grammar-driven approximate substructure matching, and 2) grammar-driven approximate tree node matching. The two approximate matching mechanisms enable the proposed kernel to better explore linguistically motivated structured knowledge. Experiments on the CoNLL-2005 SRL shared task and the PropBank I corpus show that the proposed kernel outperforms the standard convolution tree kernel significantly. Moreover, we present a composite kernel to integrate a feature-based polynomial kernel and the proposed grammar-driven convolution tree kernel for SRL. Experimental results show that our composite kernel-based method significantly outperforms the previously best-reported ones.

This paper presents a new approach based on Equivalent Pseudowords (EPs) to tackle Word Sense Disambiguation (WSD) in Chinese language. EPs are particular artificial ambiguous words, which can be used to realize unsupervised WSD. A Bayesian classifier is implemented to test the efficacy of the EP solution on Senseval-3 Chinese test set. The performance is better than state-of-the-art results with an average F-measure of 0.80. The experiment verifies the value of EP for unsupervised WSD.

With the available resources on the Internet becoming plentiful, a large amount of harmful information is permeating in and has been seriously affecting people's normal work and living. Therefore, harmful data streams must be recognized and filtered out effectively. After analyzing some harmful contents in Internet information streams, we present a new method, which recognizes specific information by machine learning (ML). We extracted key information from a number of corpuses through the ML method to obtain the part of speech (POS) transfer-form for key information by learning from corpuses, which is based on the same pronunciation matching of key information. Furthermore, the testing value of key information will be obtained in a real corpus to examine the likelihood between matching rules from information streams and those learnt from corpuses through the average value of POS transfer probability of key information. Therefore, the testing value for the whole real data stream will be obtained The experiment proved that the method was efficient for recognizing certain Internet harmful information.