Scientists typically need to take a large volume of information into account in order to deal with re-occurring tasks such as inspecting proceedings, finding related work, or reviewing papers. Our work aims at filling the gap between text documents and a structured representations of their content in the domain of resilience computing by combining computer linguistics and ontological methods. The results of our research include: a thesaurus of the domain, automatic clustering of the domain documents, a domain ontology, and a tool for constructing ontologies with the aid of domain thesauri.

In this paper, we propose a universal solution to web search and web browsing on handheld devices for visually impaired people. For this purpose, we propose (1) to automatically cluster web page results and (2) to summarize all the information in web pages so that speech-to-speech interaction is used efficiently to access information.

Semi-supervised learning is an active research field. Previous results shown that unite background information into the original unsupervised clustering problem could archive higher accuracy. In this paper, we explore the cooperation between the pairwise constrains given by the user and the sematic information in natural language. In addition, we reduce the time complexity to make the algorithm feasible for large quantities of data. Experiments on different scales of corpus show the robustness and effectiveness of the proposed algorithm, which the F-measure archives 20% higher than previous algorithms.

Numerous companies nowadays gather product improvement There are two additional modifications we use to adjust ideas. Reviewing all of the resulting thousands of our feature set, that provide improvements over the original ideas without tools would require a great deal of time and feature counts. The first is based upon our assumption that resources. Automatic tools can help these reviewers in a words in the title are more important than words in the other number of ways. The questions we address here are categorization, text fields. We simply weight unigrams and bigrams that finding common ideas, and finding idea trends over appear in the title ten times as heavily as those that appear in time. We explore techniques to answer these questions using the rest of the text.

We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm.

Multi-instance learning attempts to learn from a training set consisting of labeled bags each containing many unlabeled instances. Previous studies typically treat the instances in the bags as independently and identically distributed. However, the instances in a bag are rarely independent, and therefore a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits the relations among instances. In this paper, we propose a simple yet effective multi-instance learning method, which regards each bag as a graph and uses a specific kernel to distinguish the graphs by considering the features of the nodes as well as the features of the edges that convey some relations among instances. The effectiveness of the proposed method is validated by experiments.

Open Source Software (OSS) often relies on large repositories, like SourceForge, for initial incubation. The OSS repositories offer a large variety of meta-data providing interesting information about projects and their success. In this paper we propose a data mining approach for training classifiers on the OSS meta-data provided by such data repositories. The classifiers learn to predict the successful continuation of an OSS project. The `successfulness' of projects is defined in terms of the classifier confidence with which it predicts that they could be ported in popular OSS projects (such as FreeBSD, Gentoo Portage).

This paper presents a tree-to-tree transduction method for sentence compression. Our model is based on synchronous tree substitution grammar, a formalism that allows local distortion of the tree topology and can thus naturally capture structural mismatches. We describe an algorithm for decoding in this framework and show how the model can be trained discriminatively within a large margin framework. Experimental results on sentence compression bring significant improvements over a state-of-the-art model.

This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as ``a real bargain'' or ``good value.'' These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases.

In this paper we explore avenues for improving the reliability of dimensionality reduction methods such as Non-Negative Matrix Factorization (NMF) as interpretive exploratory data analysis tools. We first explore the difficulties of the optimization problem underlying NMF, showing for the first time that non-trivial NMF solutions always exist and that the optimization problem is actually convex, by using the theory of Completely Positive Factorization. We subsequently explore four novel approaches to finding globally-optimal NMF solutions using various ideas from convex optimization. We then develop a new method, isometric NMF (isoNMF), which preserves non-negativity while also providing an isometric embedding, simultaneously achieving two properties which are helpful for interpretation. Though it results in a more difficult optimization problem, we show experimentally that the resulting method is scalable and even achieves more compact spectra than standard NMF.