As tweets has become a comprehensive repository of fresh information, Semantic Role Labeling (SRL) for tweets has aroused great research interests because of its center role in a wide range of tweet related studies such as fine-grained information extraction, sentiment analysis and summarization. However, the fact that a tweet is often too short and informal to provide sufficient information poses a main challenge. To tackle this challenge, we propose a new method to collectively label similar tweets. The underlying idea is to exploit similar tweets to make up for the lack of information in a tweet. Specifically, similar tweets are first grouped together by clustering. Then for each cluster a two-stage labeling is conducted: One labeler conducts SRL to get statistical information, such as the predicate/argument/role triples that occur frequently, from its highly confidently labeled results; then in the second stage, another labeler performs SRL with such statistical information to refine the results. Experimental results on a human annotated dataset show that our approach remarkably improves SRL by 3.1% F1.

Field classification is an extension of the traditional classification framework, by breaking the i.i.d. assumption. In field classification, patterns occur as groups (fields) of homogeneous styles. By utilizing style consistency, classifying groups of patterns is often more accurate than classifying single patterns. In this paper, we extend the Bayes decision theory, and develop the Field Bayesian Model (FBM) to deal with field classification. Specifically, we propose to learn a Style Normalized Transformation (SNT) for each field. Via the SNTs, the data of different fields are transformed to a uniform style space (i.i.d. space). The proposed model is a general and systematic framework, under which many probabilistic models can be easily extended for field classification. To transfer the model to unseen styles, we propose a transductive model called Transfer Bayesian Rule (TBR) based on self-training. We conducted extensive experiments on face, speech and a large-scale handwriting dataset, and got significant error rate reduction compared to the state-of-the-art methods.

In contrast to the studied in single-period optimization [Miller and Wagner, standard approach that studies the expected performance, 1965; Prékopa, 1970]. However, little has been done in we consider the policy that maximizes the context of sequential decision problem including MDPs. the probability of achieving a predetermined target The standard approaches in risk-averse MDPs include maximization performance, a criterion we term probabilistic of expected utility function [Bertsekas, 1995], goal Markov decision processes. We show that and optimization of a coherent risk measure [Riedel, 2004; this problem is NPhard, but can be solved using a Le Tallec, 2007]. Both approaches lead to formulations that pseudo-polynomial algorithm. We further consider can not be solved in polynomial time, except for special a variant dubbed "chance-constraint Markov decision cases including exponential utility function [Chung and Sobel, problems," that treats the probability of achieving 1987], piecewise linear utility function with a single target performance as a constraint instead of the break down point [Liu and Koenig, 2005], and risk measures maximizing objective. This variant is NPhard, but that can be reduced to robust MDPs satisfying the socalled can be solved in pseudo-polynomial time.

There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the “word-of-mouth” effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the named entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through a graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested using real-world blog data. Experimental results show the advantage of the proposed method on tracking topics in short, noisy text.

We propose an online classification approach for co-occurrence data which is based on a simple information theoretic principle. We further show how to properly estimate the uncertainty associated with each prediction of our scheme and demonstrate how to exploit these uncertainty estimates. First, in order to abstain highly uncertain predictions. And second, within an active learning framework, in order to preserve classification accuracy while substantially reducing training set size. Our method is highly efficient in terms of run-time and memory footprint requirements. Experimental results in the domain of text classification demonstrate that the classification accuracy of our method is superior or comparable to other state-of-the-art online classification algorithms.

Consider random hypergraphs on n vertices, where each k -element subset of vertices is selected with probability $independently and randomly as a hyperedge. By sparse we mean that the total number of hyperedges is  O ( n) or O ( n  ln n ). When k = 2, these are exactly the classical Erdös-Rényi random graphs G(n,p ). We prove that with high probability, hinge width on these sparse random hypergraphs can grow linearly with the expected number of hyperedges. Some random constraint satisfaction problems such as Model RB and Model RD have satisfiability thresholds on these sparse constraint hypergraphs, thus the large hinge width results provide some theoretical evidence for random instances around satisfiability thresholds to be hard for a standard hinge-decomposition based algorithm. We also conduct experiments on these and other kinds of random graphs with several hundreds vertices, including regular random graphs and power law random graphs. The experimental results also show that hinge width can grow linearly with the number of edges on these different random graphs. These results may be of further interests.

Cooperation among automated agents is becoming increasingly important in various artificial intelligence applications. Coalitional (i.e., cooperative) game theory supplies conceptual and mathematical tools useful in the analysis of such interactions, and in particular in the achievement of stable outcomes among self-interested agents. Here, we study the minimal external subsidy required to stabilize the core of a coalitional game. Following the Cost of Stability (CoS) model introduced by Bachrach et al. [2009a], we give tight bounds on the required subsidy under various restrictions on the social structure of the game. We then compare the extended core induced by subsidies with the least core of the game, proving tight bounds on the ratio between the minimal subsidy and the minimal demand relaxation that each lead to stability.

Another example is items to users based on their historical ratings. In that, although many people don't like animations, they may real-world scenarios, user interests may drift over still have interests in emerging 3-D animations because of the time since they are affected by moods, contexts, fantastic 3-D visual effects. These observations show that, and pop culture trends. This leads to the fact that although many aspects of user interests can be found based a user's historical ratings comprise many aspects of on users' historical ratings, at a certain time slice, one user's user interests spanning a long time period. However, interest may only focus on one or a couple of aspects. Thus, at a certain time slice, one user's interest may the static CF methods built on the entire historical ratings are only focus on one or a couple of aspects. Thus, inadequate to capture user-interest drift. In order to track user CF techniques based on the entire historical ratings interests and create comprehensive user profiles such that different may recommend inappropriate items. In this paper, recommendation strategies can be used for consistenttaste we consider modeling user-interest drift over time users and changing-taste users, a CF method that can based on the assumption that each user has multiple model user interests over time is required.

We study the existence and computational complexity of coalitional stability concepts based on social networks. Our concepts represent a natural and rich combinatorial generalization of a recent notion termed partition equilibrium. We assume that players in a strategic game are embedded in a social (or, communication) network, and there are coordination constraints defining the set of coalitions that can jointly deviate in the game. A main feature of our approach is that players act in a "considerate" fashion to ignore potentially profitable (group) deviations if the change in their strategy may cause a decrease of utility to their neighbors in the network. We explore the properties of such considerate equilibria in application to the celebrated class of resource selection games (RSGs). Our main result proves existence of a super-strong considerate equilibrium in all symmetric RSGs with strictly increasing delays, for any social network among the players and feasible coalitions represented by the set of cliques. The existence proof is constructive and yields an efficient algorithm. In fact, the computed considerate equilibrium is a Nash equilibrium for a standard RSG, thus showing that there exists a state that is stable against selfish and considerate behavior simultaneously. Furthermore, we provide results on convergence of considerate dynamics.

Extracting the relations that exist between two entities is an important step in numerousWeb-related tasks such as information extraction.A supervised relation extraction system that is trained to extract a particular relation type might not accurately extract a new type of a relation for which it has not been trained.However, it is costly to create training data manually for every new relation type that one might want to extract.We propose a method to adapt an existing relation extraction system to extractnew relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.