Semantic Representation Analysis: A General Framework for Individualized, Domain-Specific and Context-Sensitive Semantic Processing

2014 ◽  
pp. 35-46 ◽  
Author(s):  
Xiangen Hu ◽  
Benjamin D. Nye ◽  
Chuang Gao ◽  
Xudong Huang ◽  
Jun Xie ◽  
...  
Keyword(s):  
General Framework ◽  
Semantic Processing ◽  
Semantic Representation ◽  
Context Sensitive ◽  
Domain Specific

A diachronic frequency account of the allomorphy of some grammatical markers

2010 ◽  
Vol 47 (1) ◽  
pp. 31-64 ◽  
Author(s):  
THOMAS BERG
Keyword(s):  
Life Expectancy ◽  
High Frequency ◽  
Relative Ease ◽  
Context Sensitive ◽  
Domain Specific ◽  
New Variant ◽  
Specific Frequency ◽  
Diachronic Analysis ◽  
Frequency Threshold ◽  
Morphological System

Allomorphy is a reaction of the morphological system to problems that the unrestrained application of inflectional and other rules creates at the phonological level. These problems are dealt with in some cases but left unattended in others. A diachronic analysis of English reveals that phonemically conditioned allomorphy originates from gradual sound change, with the old and the new variant forming a morphophonological paradigm. The historical stability as well as the synchronic motivation of allomorphy are claimed to be frequency-based. The higher the frequency, the longer the life expectancy. Synchronically, there is a (language- and domain-specific) frequency threshold above which morphophonological variation occurs and below which it fails to occur. The underlying logic of the model is that frequency encourages lexicalization at all linguistic levels. The relative ease with which high-frequency items are accessed enhances the tolerance towards formal variation, hence the emergence of natural phonological rules. As the application of these rules is context-dependent, allomorphy arises as a context-sensitive process. Repair strategies are also argued to be under the sway of frequency. Epenthesis is found in highly frequent structures while coalescence is reserved for less frequent ones. Frequency also determines the scope and the optionality of morphophonological rules. Phonemically governed allomorphy is shown to be a member of the larger family of variationist phenomena which are bound together by their sensitivity to frequency.

Neurochemical profiles of the anterior temporal lobe predict response of repetitive transcranial magnetic stimulation on semantic processing

2021 ◽  
Author(s):  
JeYoung Jung ◽  
Stephen Williams ◽  
Faezeh Sanae Nezhad ◽  
Matthew Lambon Ralph
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Temporal Lobe ◽  
Magnetic Stimulation ◽  
Semantic Processing ◽  
Cortical Excitability ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Semantic Representation ◽  
Cortical Region ◽  
Anterior Temporal Lobe ◽  
Neurochemical Profiles

Abstract The effect of repetitive transcranial magnetic stimulation can vary considerably across individuals, but the reasons for this still remain unclear. Here, we investigated whether the response to continuous theta-burst stimulation (cTBS) – an effective protocol for decreasing cortical excitability – related to individual differences in glutamate and GABA neurotransmission. We applied cTBS over the anterior temporal lobe (ATL), a hub for semantic representation, to explore the relationship between the baseline neurochemical profiles in this region and the response to this stimulation. Our experiments revealed that non-responders (subjects who did not show an inhibitory effect of cTBS on subsequent semantic performance) had higher excitatory-inhibitory balance (glutamate + glutamine/GABA ratio) in the ATL, which led to up-regulated task-induced regional activity as well as increased ATL-connectivity with other semantic regions compared to responders. These results disclose that the baseline neurochemical state of a cortical region can be a significant factor in predicting responses to cTBS.

scholarly journals Can computers understand words like humans do? Comparable semantic representation in neural and computer systems

2019 ◽  
Author(s):  
Linmin Zhang ◽  
Lingting Wang ◽  
Jinbiao Yang ◽  
Peng Qian ◽  
Xuefei Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Word Processing ◽  
Semantic Processing ◽  
Computational Models ◽  
Time Window ◽  
Early Stage ◽  
Semantic Representation ◽  
Deep Understanding ◽  
Joint Force ◽  
Strong Artificial Intelligence

AbstractSemantic representation has been studied independently in neuroscience and computer science. A deep understanding of human neural computations and the revolution to strong artificial intelligence appeal for a joint force in the language domain. We investigated comparable representational formats of lexical semantics between these two complex systems with fine temporal resolution neural recordings. We found semantic representations generated from computational models significantly correlated with EEG responses at an early stage of a typical semantic processing time window in a two-word semantic priming paradigm. Moreover, three representative computational models differentially predicted EEG responses along the dynamics of word processing. Our study provided a finer-grained understanding of the neural dynamics underlying semantic processing and developed an objective biomarker for assessing human-like computation in computational models. Our novel framework trailblazed a promising way to bridge across disciplines in the investigation of higher-order cognitive functions in human and artificial intelligence.

scholarly journals Improved Semantic Representation for Domain-Specific Entities

2016 ◽  
Author(s):  
Mohammad Taher Pilehvar ◽  
Nigel Collier
Keyword(s):  
Semantic Representation ◽  
Domain Specific

Techniques for Named Entity Recognition

2012 ◽  
pp. 191-217 ◽  
Author(s):  
Girish Keshav Palshikar
Keyword(s):  
Semantic Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Number Of Factors ◽  
Web Contents ◽  
Biological Domain

While building and using a fully semantic understanding of Web contents is a distant goal, named entities (NEs) provide a small, tractable set of elements carrying a well-defined semantics. Generic named entities are names of persons, locations, organizations, phone numbers, and dates, while domain-specific named entities includes names of for example, proteins, enzymes, organisms, genes, cells, et cetera, in the biological domain. An ability to automatically perform named entity recognition (NER) – i.e., identify occurrences of NE in Web contents – can have multiple benefits, such as improving the expressiveness of queries and also improving the quality of the search results. A number of factors make building highly accurate NER a challenging task. Given the importance of NER in semantic processing of text, this chapter presents a detailed survey of NER techniques for English text.

Techniques for Named Entity Recognition

2013 ◽  
pp. 400-426 ◽  
Author(s):  
Girish Keshav Palshikar
Keyword(s):  
Semantic Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Number Of Factors ◽  
Web Contents ◽  
Biological Domain

While building and using a fully semantic understanding of Web contents is a distant goal, named entities (NEs) provide a small, tractable set of elements carrying a well-defined semantics. Generic named entities are names of persons, locations, organizations, phone numbers, and dates, while domain-specific named entities includes names of for example, proteins, enzymes, organisms, genes, cells, et cetera, in the biological domain. An ability to automatically perform named entity recognition (NER) – i.e., identify occurrences of NE in Web contents – can have multiple benefits, such as improving the expressiveness of queries and also improving the quality of the search results. A number of factors make building highly accurate NER a challenging task. Given the importance of NER in semantic processing of text, this chapter presents a detailed survey of NER techniques for English text.

Developmental Cascades

2019 ◽  
Author(s):  
Lisa M. Oakes ◽  
David H. Rakison
Keyword(s):  
Cognitive Development ◽  
General Framework ◽  
Ripple Effect ◽  
Object Representations ◽  
Infant Cognition ◽  
Whole Child ◽  
Domain Specific ◽  
Multiple Levels ◽  
Over Time ◽  
First Words

Children take their first steps, produce their first words, and become able to solve many new problems seemingly overnight. Yet, each change reflects many other previous developments that occurred in the whole child across a range of domains, and each change, in turn, will provide opportunities for future development. This book proposes that all change can be explained in terms of developmental cascades such that events that occur at one point in development set the stage, or cause a ripple effect, for the emergence or development of different abilities, functions, or behaviors at another point in time. The authors argue that these developmental cascades are influenced by different kinds of constraints that do not have a single foundation: They may originate from the structure of the child’s nervous system and body, the physical or social environment, or knowledge and experience. These constraints occur at multiple levels of processing and change over time, and both contribute to developmental cascades and are the product of them. The book presents an overview of this developmental cascade perspective as a general framework for understanding change throughout the lifespan, although it is applied primarily to cognitive development in infancy. The book also addresses how a cascade approach obviates the dichotomy between domain-general and domain-specific mechanisms. The framework is applied in detail to three domains within infant cognitive development—namely, looking behavior, object representations, and concepts for animacy—as well as two domains unrelated to infant cognition (gender and attachment).

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