The seven essays that comprise this volume address the actual processes by which a discreet number of terms in modern Chinese and Japanese came into being, how they outdistanced all competitors, and ...the persons and texts involved in the process.
Drawing on detailed case studies across a range of languages, including English, German, Dutch, Italian, Portuguese, Polish, Czech, Russian, Lithuanian and Greek, this book examines the different ...factors that determine the outcome of the interaction between borrowing and word formation.
This landmark publication in comparative linguistics is the first comprehensive work to address the general issue of what kinds of words tend to be borrowed from other languages. The authors have ...assembled a unique database of over 70,000 words from 40 languages from around the world, 18,000 of which are loanwords. This database (http://loanwords.info) allows the authors to make empirically founded generalizations about general tendencies of word exchange among languages.
Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The ...tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems. KEYWORDS Hypernym Path, Multi-sense Embeddings, Synonym Sets, Word Embeddings, Word Similarity.
Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did ...not explore the power of the unsupervised word embedding sufficiently.
In this paper, we discuss a capsule network-based approach, taking advantage of capsule’s potential for recognizing highly overlapping features and dealing with segmentation. We propose a capsule network-based method to decompose the unsupervised word embedding of an ambiguous word into context specific sense embedding, called CapsDecE2S. In this approach, the unsupervised ambiguous embedding is fed into capsule network to produce its multiple morpheme-like vectors, which are defined as the basic semantic language units of meaning. With attention operations, CapsDecE2S integrates the word context to reconstruct the multiple morpheme-like vectors into the context-specific sense embedding. To train CapsDecE2S, we propose a sense matching training method. In this method, we convert the sense learning into a binary classification that explicitly learns the relation between senses by the label of matching and non-matching. The CapsDecE2S was experimentally evaluated on two sense learning tasks, i.e., word in context and word sense disambiguation. Results on two public corpora Word-in-Context and English all-words Word Sense Disambiguation show that, the CapsDecE2S model achieves the new state-of-the-art for the word in context and word sense disambiguation tasks. The source code can be downloaded from the Github page11https://github.com/Gdls/CapsDecE2S..
There are no uninstantiated words Miller, J. T. M.
Inquiry (Oslo),
06/2022, Letnik:
ahead-of-print, Številka:
ahead-of-print
Journal Article
Recenzirano
Odprti dostop
Kaplan (1990. "Words." Proceedings of the Aristotelian Society 64: 93-119; 2011. "Words on Words." The Journal of Philosophy 108 (9): 504-529) argues that there are no unspoken words. Hawthorne and ...Lepore (2011. "On Words." The Journal of Philosophy 108 (9): 447-485) put forward examples that purport to show that there can be such words. Here, I argue that Kaplan is correct, if we grant him a minor variation. While Hawthorne and Lepore might be right that there can be unspoken words, I will argue that they fail to show that there can be uninstantiated words.
On infinite prefix normal words Cicalese, Ferdinando; Lipták, Zsuzsanna; Rossi, Massimiliano
Theoretical computer science,
03/2021, Letnik:
859
Journal Article
Recenzirano
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•We present constructions of infinite prefix normal words.•We explore connections between infinite prefix normal words and other infinite words.•We study abelian complexity and infinite prefix normal ...words.•Prefix normal forms can be extended to infinite words.•We characterize Sturmian words which are prefix normal.
Prefix normal words are binary words with the property that no factor has more 1s than the prefix of the same length. Finite prefix normal words were introduced in Fici and Lipták (2011) 18. In this paper, we study infinite prefix normal words and explore their relationship to some known classes of infinite binary words. In particular, we establish a connection between prefix normal words and Sturmian words, between prefix normal words and abelian complexity, and between prefix normality and lexicographic order.1
In this brief commentary, we propose the emotion word type that has not been elucidated in the review (Hinojosa, J. A., Moreno, E. M., & Ferré, P. (2019). Affective neurolinguistics: Towards a ...framework for reconciling language and emotion. Language, Cognition and Neuroscience. Advance online publication. doi:10.1080/23273798.2019.1620957) should be incorporated in affective neurolinguistics. Emotion words, as a category against neutral words, are a mixture of two sub types: emotion-label words (e.g. joy, sorrow) and emotion-laden words (e.g. reward, snake). Differences between the two kinds of words have been confirmed in numerous studies. The discrepancy of the two types of words has the potential of contributing to the definition and categorisation of emotion words and provides a new interface for affective neurolinguistics, affective neuroscience, and cognitive neuroscience.
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