Results 11 to 20 of about 22,610 (167)
Linguistically Regularized LSTM for Sentiment Classification [PDF]
Sentiment understanding has been a long-term goal of AI in the past decades. This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed very recently, however, previous models either depend on expensive phrase-level annotation, whose performance drops substantially when trained with only ...
Qiao Qian +3 more
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Linguistic Regularities of LOLspeak [PDF]
The influence of the Internet on language is an unprecedented phenomenon. Maybe only the emergence of the systems of writing and the invention of printing can be claimed to have contributed more to the way language is used and perceived. Numerous publications have been devoted to the new forms of language thriving in the cyberspace.
Bury, Beata, Wojtaszek, Adam
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Sentence Analogies: Linguistic Regularities in Sentence Embeddings [PDF]
While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to
Xunjie Zhu, Gerard de Melo
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Improving statistical parsing by linguistic regularization [PDF]
Statistically-based parsers for large corpora, in particular the Penn Tree Bank (PTB), typically have not used all the linguistic information encoded in the annotated trees on which they are trained. In particular, they have not in general used information that records the effects of derivations, such as empty categories and the representation of ...
Berwick, Robert C. +1 more
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Robust Reading Comprehension With Linguistic Constraints via Posterior Regularization [PDF]
In spite of great advancements of machine reading comprehension (RC), existing RC models are still vulnerable and not robust to different types of adversarial examples. Neural models over-confidently predict wrong answers to semantic different adversarial examples, while over-sensitively predict wrong answers to semantic equivalent adversarial examples.
Mantong Zhou +2 more
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Linguistic Regularities in Sparse and Explicit Word Representations [PDF]
Recent work has shown that neuralembedded word representations capture many relational similarities, which can be recovered by means of vector arithmetic in the embedded space. We show that Mikolov et al.’s method of first adding and subtracting word vectors, and then searching for a word similar to the result, is equivalent to searching for a word ...
Omer Levy, Yoav Goldberg
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The effect of regular rhythm on the perception of linguistic and non‐linguistic auditory input
Abstract Regular distribution of auditory stimuli over time can facilitate perception and attention. However, such effects have to date only been observed in separate studies using either linguistic or non‐linguistic materials.
Rassili, Outhmane, Ordin, Mikhail
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Implicit learning of non-linguistic and linguistic regularities in children with dyslexia
One of the hallmarks of dyslexia is the failure to automatise written patterns despite repeated exposure to print. Although many explanations have been proposed to explain this problem, researchers have recently begun to explore the possibility that an underlying implicit learning deficit may play a role in dyslexia.
Nigro, Lucíana +3 more
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Detecting Regular Sound Changes in Linguistics as Events of Concerted Evolution [PDF]
Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change.
Daniel J. Hruschka +6 more
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Limited-Resource Adapters Are Regularizers, Not Linguists
Cross-lingual transfer from related high-resource languages is a well-established strategy to enhance low-resource language technologies. Prior work has shown that adapters show promise for, e.g., improving low-resource machine translation (MT). In this work, we investigate an adapter souping method combined with cross-attention fine-tuning of a pre ...
Marcell Fekete +6 more
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