Amilcare is a tool for Adaptive Information Extraction (IE) designed for supporting active annotation of documents for the Semantic Web (SW). It can be used either for unsupervised document annotation or as a support for human annotation. Amilcare is ...

There is enormous amount of multilingual documents from various sources and possibly from different countries describing a single event or a set of related events. It is desirable to construct text mining methods that can compare and highlight ...

In this paper, we present a method based on document probes to quantify and diagnose topic structure, distinguishing topics as monolithic, structured, or diffuse. The method also yields a structure analysis that can be used directly to optimize filter (...

We discuss the automatic generation of thematic lexicons by means of term categorization, a novel task employing techniques from information retrieval (IR) and machine learning (ML). Specifically, we view the generation of such lexicons as an iterative ...

The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to ...

The poster reports on a project in which we are investigating methods for breaking the human metadata-generation bottleneck that plagues Digital Libraries. The research question is whether metadata elements and values can be automatically generated from ...

This paper reports on the user-centered design methodology and techniques used for the elicitation of user requirements and how these requirements informed the first phase of the user interface design for a Cross-Language Information Retrieval System. ...

In this study we show experimental results on using Independent Component Analysis (ICA) and the Self-Organizing Map (SOM) in document analysis. Our documents are segments of spoken dialogues carried out over the telephone in a customer service, ...

We propose a self-supervised word-segmentation technique for Chinese information retrieval. This method combines the advantages of traditional dictionary based approaches with character based approaches, while overcoming many of their shortcomings. ...

Query ambiguity is a generally recognized problem, particularly in Web environments where queries are commonly only one or two words in length. In this study, we explore one technique that finds commonly occurring patterns of parts of speech near a one-...

Arabic, a highly inflected language, requires good stemming for effective information retrieval, yet no standard approach to stemming has emerged. We developed several light stemmers based on heuristics and a statistical stemmer based on co-occurrence ...

This work evaluates a few search strategies for Arabic monolingual and cross-lingual retrieval, using the TREC Arabic corpus as the test-bed. The release by NIST in 2001 of an Arabic corpus of nearly 400k documents with both monolingual and cross-...

We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the cold-start problem, where we wish to recommend ...

The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifiers. We introduce a probabilistic method for combining classifiers that ...

We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The algorithms we present are simple to implement and are time and memory ...

We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential (sIB) approach is guaranteed to converge to a local maximum of the ...

In this paper we describe a Cross Document Summarizer XDoX designed specifically to summarize large document sets (50-500 documents and more). Such sets of documents are typically obtained from routing or filtering systems run against a continuous ...

A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are useed for partitioning sentences of ...