Results 1 to 10 of about 696,980 (74)

Linguistic laws in biology [PDF]

Trends in Ecology and Evolution 37(1), 53-66 (2022), 2023
Linguistic laws, the common statistical patterns of human language, have been investigated by quantitative linguists for nearly a century. Recently, biologists from a range of disciplines have started to explore the prevalence of these laws beyond language, finding patterns consistent with linguistic laws across multiple levels of biological ...
arxiv   +1 more source

Generating semantic maps through multidimensional scaling: linguistic applications and theory [PDF]

, 2020
This paper reports on the state-of-the-art in application of multidimensional scaling (MDS) techniques to create semantic maps in linguistic research. MDS refers to a statistical technique that represents objects (lexical items, linguistic contexts, languages, etc.) as points in a space so that close similarity between the objects corresponds to close ...
arxiv   +1 more source

Three-way Decisions with Evaluative Linguistic Expressions [PDF]

arXiv, 2023
We propose a linguistic interpretation of three-way decisions, where the regions of acceptance, rejection, and non-commitment are constructed by using the so-called evaluative linguistic expressions, which are expressions of natural language such as small, medium, very short, quite roughly strong, extremely good, etc.
arxiv  

Distributional Semantics and Linguistic Theory [PDF]

Annu. Rev. Linguist. 6:213-34 (2020), 2019
Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the
arxiv   +1 more source

Parallels of human language in the behavior of bottlenose dolphins [PDF]

Linguistic Frontiers (2022) 5(1), 1-7, 2016
A short review of similarities between dolphins and humans with the help of quantitative linguistics and information theory.
arxiv   +1 more source

On the proper role of linguistically-oriented deep net analysis in linguistic theorizing [PDF]

arXiv, 2021
A lively research field has recently emerged that uses experimental methods to probe the linguistic behavior of modern deep networks. While work in this tradition often reports intriguing results about the grammatical skills of deep nets, it is not clear what their implications for linguistic theorizing should be.
arxiv  

Linguistic-style-aware Neural Networks for Fake News Detection [PDF]

arXiv, 2023
We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of ...
arxiv  

Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT [PDF]

arXiv, 2021
Retrieval-based dialogue systems select the best response from many candidates. Although many state-of-the-art models have shown promising performance in dialogue response selection tasks, there is still quite a gap between R@1 and R@10 performance. To address this, we propose to leverage linguistic coordination (a phenomenon that individuals tend to ...
arxiv  

Linguistics Computation, Automatic Model Generation, and Intensions [PDF]

arXiv, 1994
Techniques are presented for defining models of computational linguistics theories. The methods of generalized diagrams that were developed by this author for modeling artificial intelligence planning and reasoning are shown to be applicable to models of computation of linguistics theories.
arxiv  

Integrating Linguistic Theory and Neural Language Models [PDF]

arXiv, 2022
Transformer-based language models have recently achieved remarkable results in many natural language tasks. However, performance on leaderboards is generally achieved by leveraging massive amounts of training data, and rarely by encoding explicit linguistic knowledge into neural models.
arxiv