In this paper we present BabelNet – a very large, wide-coverage multilingual semantic network. The resource is automatically constructed by means of a methodology that integrates lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition Machine Translation is also applied to enrich the resource with lexical information for all languages. We conduct experiments on new and existing gold-standard datasets to show the high quality and coverage of the resource. 1

One of the main obstacles to highperformance Word Sense Disambiguation (WSD) is the knowledge acquisition bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of semantic relations from an encyclopedic resource, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, simple knowledge-lean disambiguation algorithms compete with state-of-the-art supervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets. 1

Bilingual machine-readable dictionaries are knowledge resources useful in many automatic tasks. However, compared to monolingual computational lexicons like WordNet, bilingual dictionaries typically provide a lower amount of structured information such as lexical and semantic relations, and often do not cover the entire range of possible translations for a word of interest. In this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the automated disambiguation of ambiguous translations in the lexical entries of a bilingual machine-readable dictionary. The dictionary is represented as a graph, and cyclic patterns are sought in this graph to assign an appropriate sense tag to each translation in a lexical entry. Further, we use the algorithm’s output to improve the quality of the dictionary itself, by suggesting accurate solutions to structural problems such as misalignments, partial alignments and missing entries. Finally, we successfully apply CQC to the task of synonym extraction. 1.

Knowledge on word meanings and their relations across languages is vital for enabling semantic information technologies: in fact, the ever increasingly multilingual nature of the Web now calls for the development of methods that are both robust and widely applicable for processing textual information in a multitude of languages. In our research, we approach this ambitious task by means of BabelNet, a widecoverage multilingual lexical knowledge base. In this paper we present an Application Programming Interface and a Graphical User Interface which, respectively, allow programmatic access and visual exploration of BabelNet. Our contribution is to provide the research community with easy-to-use tools for performing multilingual lexical semantic analysis, thereby fostering further research in this direction.

In this paper we present an API for programmatic access to BabelNet – a wide-coverage multilingual lexical knowledge base – and multilingual knowledge-rich Word Sense Disambiguation (WSD). Our aim is to provide the research community with easy-to-use tools to perform multilingual lexical semantic analysis and foster further research in this direction.

Summarization has always been an important task. With the swift increase in the amount of available information on the Web every day it has been even more important than the times manual summarization was feasible. Automatic summarization appears as a good candidate to resolve the information overload problem in an effective way. This project investigates approaches to generation of a summary from multiple documents/texts. It will first provide a detailed survey of summarization approaches. For this purpose, a set of important dimensions will be formulated along which various summarization approaches can be categorized, analyzed, and evaluated. The project will then focus on the design and implementation of a framework that can be used to evaluate and compare the performance of different approaches. The framework will include

This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.

Abstract—Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics and many real-world applications, word sense disambiguation has been extensively studied in Natural Language Processing and Computational Linguistics. However, existing methods either narrowly focus on a few specific words due to their reliance on expensive manually annotated training text, or give only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for real-world natural language processing applications. In this paper we present a fully automatic disambiguation method that utilizes two readily available knowledge sources: a dictionary and knowledge extracted from unannotated text. Such an automatic approach overcomes the knowledge acquisition bottleneck and makes broad-coverage word sense disambiguation feasible in practice. Evaluated with two large scale WSD evaluation corpora, our system significantly outperforms the best unsupervised system and achieves the similar performance as the top-performing supervised systems.

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and other research outputs Automatically structuring domain knowledge from text: a review of current research.