Композициональность

Linguistic access to uncertain quantita- tive knowledge about physical properties is provided by dimensional adjectives, e.g. long-short in the spatial and tempo- ral senses, near-far, fast-slow, etc. Seman- tic analyses …...

In many applications, replacing a complex word form by its stem can reduce sparsity, re- vealing connections in the data that would not otherwise be apparent. In this paper, we …...

We propose an unsupervised system that learns continuous degrees of lexicality for noun-noun compounds, beating a strong base- line on several tasks. We demonstrate that the distributional representations of compounds …...

In this article, we describe a new approach to distributional semantics. This approach relies on a generative model of sentences with latent variables, which takes the syntax into account by …...

AbstractThe building of distributional thesauri from corpora is a problem that was the focus of a significant number of articles, starting with (Grefenstette, 1994) and followed by (Lin, 1998), (Curran …...

AbstractThe lexicon of any natural language encodes a huge number of distinct word meanings. Just to understand this article, you will need to know what thousands of words mean. The …...

Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of …...

We introduce a new language learning setting relevant to building adaptive nat- ural language interfaces. It is inspired by Wittgenstein’s language games: a hu- man wishes to accomplish some task …...

In this paper, we explore the role of con- stituent properties in English and Ger- man noun-noun compounds (corpus fre- quencies of the compounds and their con- stituents; productivity and …...

While neural networks have been successfully applied to many NLP tasks the resulting vector- based models are very difficult to interpret. For example it’s not clear how they achieve compositionality, …...

AbstractPrevious work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect …...

AbstractThe compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of …...

AbstractNatural language is characterized by compositionality: the meaning of a complex expression is constructed from the meanings of its constituent parts. To facilitate the evaluation of the compositional abilities of …...

AbstractNatural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as compositionality. In this paper, we study …...

AbstractPredicting the degree of compositionality of noun compounds such as “snowball” and “butterfly” is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compound should …...

AbstractCompositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed by measuring meaning-form correlation. …...

AbstractWe investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of …...

Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in these models reflect heavy influences of lexi- …...

AbstractCompositionality, the phenomenon where the meaning of a phrase can be derived from its constituent parts, is a hallmark of human language. At the same time, many phrases are non-compositional, …...

AbstractCompositionality— the ability to combine familiar units like words into novel phrases and sentences— has been the focus of intense interest in artificial intelligence in recent years. To test compositional …...

AbstractCompositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks. This study empirically addresses this question by …...

AbstractDespite the tremendous success of Neural Machine Translation (NMT), its performance on low- resource language pairs still remains subpar, partly due to the limited ability to handle previously unseen inputs, …...