2,609 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
Sequence to Sequence Learning for Query Expansion
Using sequence to sequence algorithms for query expansion has not been
explored yet in Information Retrieval literature nor in Question-Answering's.
We tried to fill this gap in the literature with a custom Query Expansion
engine trained and tested on open datasets. Starting from open datasets, we
built a Query Expansion training set using sentence-embeddings-based Keyword
Extraction. We therefore assessed the ability of the Sequence to Sequence
neural networks to capture expanding relations in the words embeddings' space.Comment: 8 pages, 2 figures, AAAI-19 Student Abstract and Poster Progra
Extending the adverbial coverage of a NLP oriented resource for French
This paper presents a work on extending the adverbial entries of LGLex: a NLP
oriented syntactic resource for French. Adverbs were extracted from the
Lexicon-Grammar tables of both simple adverbs ending in -ment '-ly' (Molinier
and Levrier, 2000) and compound adverbs (Gross, 1986; 1990). This work relies
on the exploitation of fine-grained linguistic information provided in existing
resources. Various features are encoded in both LG tables and they haven't been
exploited yet. They describe the relations of deleting, permuting, intensifying
and paraphrasing that associate, on the one hand, the simple and compound
adverbs and, on the other hand, different types of compound adverbs. The
resulting syntactic resource is manually evaluated and freely available under
the LGPL-LR license.Comment: Proceedings of the 5th International Joint Conference on Natural
Language Processing (IJCNLP'11), Chiang Mai : Thailand (2011
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