TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: The folksonomy is the result of free personal information or assignment of tags to an object (determined by the URI) in order to find them. The practice of tagging is done in a collective environment. Folksonomies are self constructed, based on co-occurrence of definitions, rather than a hierarchical structure of the data. The downside of this was that a few sites and applications are able to successfully exploit the sharing of bookmarks. The need for tools that are able to resolve the ambiguity of the definitions is becoming urgent as the need of simple instruments for their visualization, editing and exploitation in web applications still hinders their diffusion and wide adoption. An intelligent interactive interface design for folksonomies should consider the contextual design and inquiry based on a concurrent interaction for a perceptual user interfaces. To represent folksonomies a new concept structure called "Folksodriven" is used in this paper. While it is presented the Folksodriven Structure Network (FSN) to resolve the ambiguity of definitions of folksonomy tags suggestions for the user. On this base a Human-Computer Interactive (HCI) systems is developed for the visualization, navigation, updating and maintenance of folksonomies Knowledge Bases the FSN through the web. System functionalities as well as its internal architecture will be introduced.

SOLUTION: Intelligent Interface Architectures for Folksonomy Driven Structure Network

PROBLEM: In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, participants as latent topics and learns those frame and event transitions that best explain the text. The number of frames is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produced state-of-the-art results while substantially reducing engineering effort.

SOLUTION: Probabilistic Frame Induction

PROBLEM: Recent advances have made it feasible to apply the stochastic variational paradigm to a collapsed representation of latent Dirichlet allocation (LDA). While the stochastic variational paradigm has successfully been applied to an uncollapsed representation of the hierarchical Dirichlet process (HDP), no attempts to apply this type of inference in a collapsed setting of non-parametric topic modeling have been put forward so far. In this paper we explore such a collapsed stochastic variational Bayes inference for the HDP. The proposed online algorithm is easy to implement and accounts for the inference of hyper-parameters. First experiments show a promising improvement in predictive performance. 1 Background We begin by considering a model where each document d is a mixture d of K discrete topicdistributions k over a vocabulary of V terms. Let zdi {1, ..,K} denote the topic of the i word wdi {1, .., V } in document d {1, .., D} and place Dirichlet priors on the parameters d, k. We have zdi | d Discrete(d) , d Dirichlet() , wdi | zdi, {k} Discrete(zdi) , k Dirichlet() , where is the top-level distribution over topics, and and are concentration parameters. While the dimensionality of K is fixed in latent Dirichlet allocation (LDA), we want the model to determine the number of topics needed. Consequently we follow the assumptions made by the hierarchical Dirichlet process (HDP) [1] of a countable but infinite number of topics, of which only a finite number is used in the posterior. Our prior is constructed by a truncated sick-breaking process [2],

SOLUTION:
Practical Collapsed Stochastic Variational Inference for the HDP