Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

This paper describes the University of North Texas SUBFINDER system. The system is able to provide the most likely set of substitutes for a word in a given context, by combining several techniques and knowledge sources. SUBFINDER has successfully participated in the best and out of ten (oot) tracks in the SEMEVAL lexical substitution task, consistently ranking in the first or second place. 1

This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, we show that our methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features. 1

Abstract. This paper describes the Italian Lexical Substitution task organised for EVALITA 2009. In this task, given a word in a specific context, the participant is asked to provide the synonyms which best fit in that context. The motivation behind the task, its objectives, the data prepared and distributed to participants, the baselines developed and the evaluation measures used are introduced. The results obtained both by the participating systems and by the baselines are presented. Finally, the different methodologies and resources used by the participants ’ systems and the results obtained by each of them are discussed.

Distributional similarity provides a technique for obtaining semantically related words from corpus data using automated methods that compare the contexts in which the words appear. Such methods can be useful for producing thesauruses, with application to work in lexicography and computational linguistics. However, the most similar words produced using these methods are not always near synonyms, but may be words in other semantic relationships: antonyms, hyponyms or even looser 'topical ' relations. This means that manual post-processing of such automatically produced resources to filter out unwanted words may be necessary before they can be used. This paper evaluates the performance of distributional methods for finding synonyms on the English Lexical Substitution Task, a lexical paraphrasing task where it is necessary to generate candidate synonyms for a target word and then select a suitable substitute on the basis of contextual information. We examine the performance of distributional methods for the first step of generating candidate synonyms and leave the second step of choosing a candidate on the basis of context for future work. A number of automated distributional methods are compared to techniques that make use of manually produced thesauruses. We demonstrate that while the performance of such automatic thesaurus acquisition methods is often below manually produced resources, precision can be greatly increased by using two automatic methods in combination. This approach gives precision results that surpass methods that exploit manually constructed resources for the same task, albeit at the expense of coverage. We conclude that such an approach to increase the precision of automatic methods to find near synonyms could improve the use of distributional methods in lexicography. 1