We present a QA system in which question analysis, off-line answer extraction and reranking and identification of potential answers from an IR all make use of syntactic dependency relations. We show that the addition of equivalence rules over patterns of dependency relations, which capture cases where different syntactic patterns express the same semantic relationship, improves the performance of various modules of the system. Keywords: Reasoning, Language Processing.

This paper presents SemFrame, a system that induces frame semantic verb classes from WordNet and LDOCE. Semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many languagebased applications. When compared

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.

AnswerFinder combines lexical, syntactic, and semantic information in various stages of the question answering process. The candidate sentences are preselected on the basis of (i) the presence of named entity types compatible with the expected answer type, and (ii) a score combination of the overlap of words, grammatical relations, and flat logical forms. The candidate answers, in turn, are extracted from (i) the set of compatible named entities and (ii) the output of a logical-form pattern matching algorithm. 1

State-of-the-art Question Answering (QA) systems are very sensitive to variations in the phrasing of an information need. Finding the preferred language for such a need is a valuable task. We investigate that claim by adopting a simple MTbased paraphrasing technique and evaluating QA system performance on paraphrased questions. We found a potential increase of 35 % in MRR with respect to the original question. 1

We present an approach to summarisation based on the use of a question answering system to select the most relevant sentences. We used AnswerFinder, a question answering system that is being developed at Macquarie University. The sentences returned by AnswerFinder are further re-ranked and collated to produce the final summary. This system will serve as a baseline upon which we intend to develop methods more specific to the task of questiondriven summarisation. 1

We describe a preliminary version of Mutaphrase, a system that generates paraphrases of semantically labeled input sentences using the semantics and syntax encoded in FrameNet, a freely available lexicosemantic database. The algorithm generates a large number of paraphrases with a wide range of syntactic and semantic distances from the input. For example, given the input “I like eating cheese”, the system outputs the syntactically distant “Eating cheese is liked by me”, the semantically distant “I fear sipping juice”, and thousands of other sentences. The wide range of generated paraphrases makes the algorithm ideal for a range of statistical machine learning problems such as machine translation and language modeling as well as other semanticsdependent tasks such as query and language generation. 1

Paraphrase recognition is used in a number of applications such as tutoring systems, question answering systems, and information retrieval systems. The context of our research is the iSTART reading strategy trainer for science texts, which needs to understand and recognize the trainee’s input and respond appropriately. This paper describes the motivation for paraphrase recognition and develops a definition of the strategy as well as a recognition model for paraphrasing. Lastly, we discuss our preliminary implementation and research plan. 1

Abstract. In the world of globalization, it is essential for companies to be able to effectively manage their knowledge capital. Being capable to effectively create, store and retrieve institutional information is a crucial competitive advantage. Readily accessible Knowledge is needed in many business ’ aspect and tasks: support decision making, profile work processes, empower in-house knowledge workers (as well as external partners and clients). In this paper we focus on the importance of terminology management as one vital aspect within a corporate Knowledge Management strategy. 1

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 very 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. In this thesis, we focus on paraphrase and textual entailment recognition, as well as paraphrase generation. We propose three paraphrase and textual entailment recognition methods, experimentally evaluated on existing benchmarks. The key idea is that by capturing similarities at various abstractions of the inputs, we can recognize paraphrases and textual entailment reasonably well. Additionally, we exploit WordNet and use features that operate on the syntactic level of the language expressions. The best