1,095 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
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.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Selectional Restrictions in HPSG
Selectional restrictions are semantic sortal constraints imposed on the
participants of linguistic constructions to capture contextually-dependent
constraints on interpretation. Despite their limitations, selectional
restrictions have proven very useful in natural language applications, where
they have been used frequently in word sense disambiguation, syntactic
disambiguation, and anaphora resolution. Given their practical value, we
explore two methods to incorporate selectional restrictions in the HPSG theory,
assuming that the reader is familiar with HPSG. The first method employs HPSG's
Background feature and a constraint-satisfaction component pipe-lined after the
parser. The second method uses subsorts of referential indices, and blocks
readings that violate selectional restrictions during parsing. While
theoretically less satisfactory, we have found the second method particularly
useful in the development of practical systems
Evelyne Lutton, Nathalie Perrot, Alberto Tonda: Evolutionary algorithms for food science and technology, Wiley, 2016, 182 pp, ISBN: 978-1-119-13683-5 [Book review]
A review of the book: Evolutionary algorithms for food science and technology, by Evelyne Lutton, Nathalie Perrot, Alberto Tond
Towards security monitoring patterns
Runtime monitoring is performed during system execution to detect whether the system’s behaviour deviates from that described by requirements. To support this activity we have developed a monitoring framework that expresses the requirements to be monitored in event calculus – a formal temporal first order language. Following an investigation of how this framework could be used to monitor security requirements, in this paper we propose patterns for expressing three basic types of such requirements, namely confidentiality, integrity and availability. These patterns aim to ease the task of specifying confidentiality, integrity and availability requirements in monitorable forms by non-expert users. The paper illustrates the use of these patterns using examples of an industrial case study
Deep Learning for User Comment Moderation
Experimenting with a new dataset of 1.6M user comments from a Greek news
portal and existing datasets of English Wikipedia comments, we show that an RNN
outperforms the previous state of the art in moderation. A deep,
classification-specific attention mechanism improves further the overall
performance of the RNN. We also compare against a CNN and a word-list baseline,
considering both fully automatic and semi-automatic moderation
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