BibSonomy publications for /user/brightbyte/nlpTue May 20 11:16:21 CEST 2008Conference on Recent Advances in Natural Language Processing212--219Roget's thesaurus and semantic similarity2003knowledge nlp ontology thesaurus We have implemented a system that measures semantic similarity using a computerized 1987 Roget's Thesaurus, and evaluated it by performing a few typical tests. We compare the results of these tests with those produced by WordNet-based similarity measures. One of the benchmarks is Miller and Charles� list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough�s 65 pairs, which we have also studied. Our Roget�s-based system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget�s and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader�s Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75\%, 82.00\% and 74.33\% of the questions respectively.Tue May 20 11:12:50 CEST 2008NIE BEZ ZNACZENIA. Prace ofiarowane Profesorowi Zygmuntowi Saloniemu z okazji{Roget’s Thesaurus as an Electronic Lexical Knowledge Base}2001nlp knowledge thesaurus Wed Mar 26 13:58:58 CET 2008{Electric Words: Dictionaries, Computers, and Meanings}1996thesaurus nlp dictionary knowledge WW-CITED WW-SHOULD taxonomie Mon Mar 24 14:09:18 CET 2008Conceptual Structures: Logical, Linguistic, and Computational Issues510--524{An Introduction to SNePS 3}18672000reasoning WW-SHOULD nlp semantic logic knowledge WW-CITED This paper provides an introduction to SNePS 3, the latest entry in the SNePS family of knowledge representation and reasoning (KRR) systems. The emphasis is on SNePS 3 as an example of a logic-based network KRR system.Wed Feb 27 01:24:28 CET 2008Int. Sym. Wikisconf/wikis/2007165-176Connecting wikis and natural language processing systems.2007nlp integration wiki READ Wed Feb 27 00:39:46 CET 2008Biannual Conference of the Society for Computational Linguistics and Language TechnologyAnalyzing and Accessing Wikipedia as a Lexical Semantic Resource2007mining wikipedia nlp semantic READ WW-MUST API We analyze Wikipedia as a lexical semantic resource and compare it with conventional resources, such as dictionaries, thesauri, semantic wordnets, etc. Different parts of Wikipedia reflect different aspects of these resources. We show that Wikipedia contains a vast amount of knowledge about, e.g., named entities, domain specific terms, and rare word senses. If Wikipedia is to be used as a lexical semantic resource in large-scale NLP tasks, efficient programmatic access to the knowledge therein is required. We review existing access mechanisms and show that they are limited with respect to performance and the provided access functions. Therefore, we introduce a general purpose, high performance Java-based Wikipedia API that overcomes these limitations. It is available for research purposes at http://www.ukp.tu-darmstadt.de/software/WikipediaAPI.Tue Feb 26 11:54:51 CET 2008Information Retrieval TechnologyPoor Man’s Stemming: Unsupervised Recognition of Same-Stem Words2006stemming nlp We present a new fully unsupervised human-intervention- free algorithm for stemming for an open class of languages. Since it does not rely on existing large data collections or other linguistic resources than raw text it is especially attractive for low-density languages. The stemming problem is formulated as a decision whether two given words are variants of the same stem and requires that, if so, there is a con- catenative relation between the two. The underlying theory makes no assumptions on whether the language uses a lot of morphology or not, whether it is prefixing or suffixing, or whether affixes are long or short. It does however make the assumption that 1. salient affixes have to be frequent, 2. words essentially are variable length sequences of random characters, and furthermore 3. that a heuristic on what constitutes a systematic affix alteration is valid. Tested on four typologically distant languages, the stemmer shows very promising results in an evaluation against a human-made gold standard.Tue Feb 26 11:38:19 CET 2008Combining Linguistic and Statistical Analysis to Extract Relations from Web Documents2007statistics nlp knowledge-extraction pattern-matching text-mining Sun Feb 10 02:19:38 CET 2008Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South KoreaNovember575--588LNCSPORE: Positive-Only Relation Extraction from Wikipedia Text48252007semantic-web iswc, wikipedia text-mining nlp knowledge-extraction annotation Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.Sun Feb 10 02:19:38 CET 2008Morristown, NJ, USAProceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics82--88Named entity transliteration and discovery from multilingual comparable corpora2006named-entities nlp translation Sun Feb 10 02:19:38 CET 2008A simple introduction to maximum entropy models for natural language processing1997nlp statistics Many problems in natural language processing can be viewed as linguistic classification problems, in which linguistic contexts are used to predict linguistic classes. Maximum entropy models offer a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context. This report demonstrates the use of a particular maximum entropy model on an example problem, and then proves some relevant...Sun Feb 10 02:19:38 CET 2008Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)Analysis of the Wikipedia Category Graph for NLP Applications2007relatedness WW-MUST nlp READ wikipedia semantic In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.