منابع مشابه
Paraphrastic Language Models
Natural languages are known for their expressive richness. Many sentences can be used to represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage and generalization, for example, when using n-gram language models (LMs). This paper proposes a novel form of language model, the paraphrastic LM, that addresses these issues. A phras...
متن کاملParaphrastic Grammars
Arguably, grammars which associate natural language expressions not only with a syntactic but also with a semantic representation, should do so in a way that capture paraphrasing relations between sentences whose core semantics are equivalent. Yet existing semantic grammars fail to do so. In this paper, we describe an ongoing project whose aim is the production of a “paraphrastic grammar” that ...
متن کاملFrameNet+: Fast Paraphrastic Tripling of FrameNet
We increase the lexical coverage of FrameNet through automatic paraphrasing. We use crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision resource. Our expanded FrameNet contains an additional 22K lexical units, a 3-fold increase over the current FrameNet, and achieves 40% better coverage when evaluated in a practical setting on New York Times data.
متن کاملParaphrastic Reformulations in Spoken Corpora
Our work addresses the automatic detection of paraphrastic reformulation in French spoken corpora. The proposed approach is syntagmatic. It is based on specific markers and the specificities of the spoken language. Manual multi-dimensional annotation performed by two annotators provides fine-grained reference data. An automatic method is proposed in order to decide whether sentences contain or ...
متن کاملTowards Universal Paraphrastic Sentence Embeddings
We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We compare six compositional architectures, evaluating them on annotated textual similarity datasets drawn both from the same distribution as the training data and from a wide range of other domains. We find that the most complex ar...
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ژورنال
عنوان ژورنال: Computer Speech & Language
سال: 2014
ISSN: 0885-2308
DOI: 10.1016/j.csl.2014.04.004