scholarly journals Is LF really a linguistic level?

2002 ◽  
Vol 25 (6) ◽  
pp. 680-680 ◽  
Author(s):  
Nick Chater
Keyword(s):  
Language Acquisition ◽  
Language Processing ◽  
Logical Form ◽  
General Purpose ◽  
Logical Problem ◽  
Modular Reasoning ◽  
Linguistic Level

Carruthers’ argument depends on viewing logical form as a linguistic level. But logical form is typically viewed as underpinning general purpose inference, and hence as having no particular connection to language processing. If logical form is tied directly to language, two problems arise: a logical problem concerning language acquisition and the empirical problem that aphasics appear capable of cross-modular reasoning.

Language acquisition recapitulates language evolution?

2008 ◽  
Vol 31 (5) ◽  
pp. 532-533 ◽  
Author(s):  
Teresa Satterfield
Keyword(s):  
Language Acquisition ◽  
Cognitive Processes ◽  
Language Evolution ◽  
General Purpose ◽  
Learning Mechanisms ◽  
Logical Problem ◽  
Domain Specific ◽  
L1 Acquisition ◽  
Specific Learning

AbstractChristiansen & Chater (C&C) focus solely on general-purpose cognitive processes in their elegant conceptualization of language evolution. However, numerous developmental facts attested in L1 acquisition confound C&C's subsequent claim that the logical problem of language acquisition now plausibly recapitulates that of language evolution. I argue that language acquisition should be viewed instead as a multi-layered construction involving the interplay of general and domain-specific learning mechanisms.

scholarly journals Language Usage and Second Language Morphosyntax: Effects of Availability, Reliability, and Formulaicity

2021 ◽  
Vol 12 ◽  
Author(s):  
Rundi Guo ◽  
Nick C. Ellis
Keyword(s):  
Second Language ◽  
Language Acquisition ◽  
Language Processing ◽  
Fixed Effects ◽  
Second Language Learners ◽  
Large Body ◽  
Elicited Imitation ◽  
Short Delay ◽  
Word Forms ◽  
Generalized Linear Mixed Effects

A large body of psycholinguistic research demonstrates that both language processing and language acquisition are sensitive to the distributions of linguistic constructions in usage. Here we investigate how statistical distributions at different linguistic levels – morphological and lexical (Experiments 1 and 2), and phrasal (Experiment 2) – contribute to the ease with which morphosyntax is processed and produced by second language learners. We analyze Chinese ESL learners’ knowledge of four English inflectional morphemes: -ed, -ing, and third-person -s on verbs, and plural -s on nouns. In Elicited Imitation Tasks, participants listened to length- and difficulty-matched sentences each containing one target morpheme and typed the whole sentence as accurately as they could after a short delay. Experiment 1 investigated lexical and morphemic levels, testing the hypotheses that a morpheme is expected to be more easily processed when it is (1) highly available (i.e., occurring in frequent word-forms), and (2) highly reliable (i.e., occurring in lemma words that are consistently conjugated in the form containing this morpheme). Thirty sentences were made for each morpheme, divided into three Availability-Reliability Distribution (ARD) groups on the basis of corpus analysis in the Corpus of Contemporary American English (COCA; Davies, 2008-): 10 target words high in availability, 10 high in reliability, and 10 low in both reliability and availability. Responses were scored on whether the target morpheme was accurately reproduced given the provision of the correct lemma. A generalized linear mixed-effects logit model (GLMM) revealed fixed effects of morpheme type, availability, and reliability on the accuracy of morpheme provision. There were no effects of lemma frequency. Experiment 2 successfully replicated these results and extended the investigation to explore phrasal formulaicity by manipulating the frequency of the four-word strings in which the morpheme was embedded. GLMMs replicated the effects of word-form availability and reliability and additionally revealed independent phrase-superiority effects where morphemes were better reproduced in contexts of higher string-frequency. Taken together, these findings demonstrate that morpheme acquisition reflects the distributional properties of learners’ experience and the mappings therein between lexis, morphology, phraseology, and semantics. These conclusions support an emergentist view of the statistical symbolic learning of morphology where language acquisition involves the satisfaction of competing constraints across multiple grain-sizes of units.

scholarly journals ANALISIS GANGGUAN BERBAHASA PADA ANAK DI KECAMATAN PAHAE JULU

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Monika Sales Sitompul
Keyword(s):  
Language Acquisition ◽  
Case Studies ◽  
Language Processing ◽  
Research Method ◽  
Language Disorders ◽  
Descriptive Research ◽  
Acquisition Device ◽  
The Brain

This study originated from cases of language disorders that occur in society in Pahae Julu district. Language is a need to interact, and humans have been blessed with Language Acquisition Device (LAD) or any language by god. However, if when speaking of someone impaired both LAD and language processing part of the brain, then the communication will not be smooth. The language disorders can happen to anyone. The purpose of this study is to reveal some kinds of language disorders, cases of language disorders and to find out the causes of language disorders experienced by the community in Pahae Julu. The method used in this research is descriptive research method type of case studies.

scholarly journals Emerging trends: A gentle introduction to fine-tuning

2021 ◽  
Vol 27 (6) ◽  
pp. 763-778
Author(s):  
Kenneth Ward Church ◽  
Zeyu Chen ◽  
Yanjun Ma
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
General Purpose ◽  
Fine Tuning ◽  
Language Engineering ◽  
Training Models ◽  
Emerging Trends ◽  
Foundation Model ◽  
Programming Skills

AbstractThe previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.

Phonotactics and Syllable Structure in Infant Speech Perception

2016 ◽  
Author(s):  
Tania S. Zamuner ◽  
Viktor Kharlamov
Keyword(s):  
Speech Perception ◽  
Language Acquisition ◽  
Language Processing ◽  
Syllable Structure ◽  
Structure Form ◽  
Modeling Studies ◽  
Wide Range ◽  
Infant Speech Perception ◽  
Sound Patterns ◽  
Linguistic Structures

Phonotactics and syllable structure form an integral part of phonological competence and may be used to discover other aspects of language. Given the importance of such knowledge to the process of language acquisition, numerous studies have investigated the development of phonotactic and syllabic knowledge in order to determine when infants become sensitive to these sound patterns and how they may use this knowledge in language processing. Considering that infants’ first exposure to linguistic structures comes from speech perception, we provide an overview of the perception-related issues that have been investigated experimentally and point out issues that have not yet been addressed in the literature. We begin with phonotactic development, examining a wide range of sound patterns, followed by a discussion of the acquisition of syllable structure and a brief summary of various outstanding issues that may be of interest to the reader, including production-related investigations and phonological modeling studies.

Geoscience Language Processing for Exploration

2021 ◽  
Author(s):  
Huseyin Denli ◽  
Hassan A Chughtai ◽  
Brian Hughes ◽  
Robert Gistri ◽  
Peng Xu
Keyword(s):  
Language Processing ◽  
Similarity Search ◽  
Question Answering ◽  
Language Translation ◽  
Automated Analysis ◽  
General Purpose ◽  
Step Change ◽  
Domain Specific ◽  
Specific Meaning ◽  
Processing Solution

Abstract Deep learning has recently been providing step-change capabilities, particularly using transformer models, for natural language processing applications such as question answering, query-based summarization, and language translation for general-purpose context. We have developed a geoscience-specific language processing solution using such models to enable geoscientists to perform rapid, fully-quantitative and automated analysis of large corpuses of data and gain insights. One of the key transformer-based model is BERT (Bidirectional Encoder Representations from Transformers). It is trained with a large amount of general-purpose text (e.g., Common Crawl). Use of such a model for geoscience applications can face a number of challenges. One is due to the insignificant presence of geoscience-specific vocabulary in general-purpose context (e.g. daily language) and the other one is due to the geoscience jargon (domain-specific meaning of words). For example, salt is more likely to be associated with table salt within a daily language but it is used as a subsurface entity within geosciences. To elevate such challenges, we retrained a pre-trained BERT model with our 20M internal geoscientific records. We will refer the retrained model as GeoBERT. We fine-tuned the GeoBERT model for a number of tasks including geoscience question answering and query-based summarization. BERT models are very large in size. For example, BERT-Large has 340M trained parameters. Geoscience language processing with these models, including GeoBERT, could result in a substantial latency when all database is processed at every call of the model. To address this challenge, we developed a retriever-reader engine consisting of an embedding-based similarity search as a context retrieval step, which helps the solution to narrow the context for a given query before processing the context with GeoBERT. We built a solution integrating context-retrieval and GeoBERT models. Benchmarks show that it is effective to help geologists to identify answers and context for given questions. The prototype will also produce a summary to different granularity for a given set of documents. We have also demonstrated that domain-specific GeoBERT outperforms general-purpose BERT for geoscience applications.

Language processing and language acquisition through the lens of Relational Morphology

2019 ◽  
pp. 201-232
Author(s):  
Ray Jackendoff ◽  
Jenny Audring
Keyword(s):  
Language Acquisition ◽  
Language Processing ◽  
Language Production ◽  
Long Term Memory ◽  
Spreading Activation ◽  
Linguistic Representations ◽  
Memory Network ◽  
Complex Words

This chapter asks what is happening to linguistic representations during language use, and how representations are formed in the course of language acquisition. It is shown how Relational Morphology’s theory of representations can be directly embedded into models of processing and acquisition. Central is that the lexicon, complete with schemas and relational links, constitutes the long-term memory network that supports language production and comprehension. The chapter first discusses processing: the nature of working memory; promiscuous (opportunistic) processing; spreading activation; priming; probabilistic parsing; the balance between storage and computation in recognizing morphologically complex words; and the role of relational links and schemas in word retrieval. It then turns to acquisition, which is to be thought of as adding nodes and relational links to the lexical network. The general approach is based on the Propose but Verify procedure of Trueswell et al. (2013), plus conservative generalization, as in usage-based approaches.

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