We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks. (C) 2012 Published by Elsevier B.V
We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems.
Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. We present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet on some datasets. We also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, we show that our method can be easily used for languages other than English by computing semantic relatedness for a German dataset.
We propose a graph-based semantic model for representing document content. Our method relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring fine-grained information about entities and their semantic relations, thus resulting in a knowledge-rich document model. We demonstrate the benefits of these semantic representations in two tasks: entity ranking and computing document semantic similarity. To this end, we couple DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and compute semantic similarity using a Graph Edit Distance based measure, which finds the optimal matching between the documents' entities using the Hungarian method. Experimental results show that our general model outperforms baselines built on top of traditional methods, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.
Recent years have seen a great deal of work that exploits collaborative, semi-structured content for Artificial Intelligence (AI) and Natural Language Processing (NLP). This special issue of the Artificial Intelligence Journal presents a variety of state-of-the-art contributions, each of which illustrates the substantial impact that work on leveraging semi-structured content is having on AI and NLP as it continuously fosters new directions of cutting-edge research. We contextualize the papers collected in this special issue by providing a detailed overview of previous work on collaborative, semi-structured resources. The survey is made up of two main logical parts: in the first part, we present the main characteristics of collaborative resources that make them attractive for AI and NLP research; in the second part, we present an overview of how these features have been exploited to tackle a variety of long-standing issues in the two fields, in particular the acquisition of large amounts of machine-readable knowledge, and its application to a wide range of tasks. The overall picture shows that not only are semi-structured resources enabling a renaissance of knowledge-rich AI techniques, but also that significant advances in high-end applications that require deep understanding capabilities can be achieved by synergistically exploiting large amounts of machine-readable structured knowledge in combination with sound statistical AI and NLP techniques. © 2012 Elsevier B.V. All rights reserved
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