278 research outputs found
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Context Search Algorithm for Lexical Knowledge Acquisition
This work was supported by Polish Committee for Scientific Research grant N516 035 31/3499.A Context Search algorithm used for lexical knowledge acquisition is presented.
Knowledge representation based on psycholinguistic theories of cognitive processes
allows for implementation of a computational model of semantic memory
in the form of semantic network. A knowledge acquisition using supervised dialog
templates have been performed in a word game designed to guess the concept
a human user is thinking about. The game, that has been implemented on a
web server, demonstrates elementary linguistic competencies based on lexical
knowledge stored in semantic memory, enabling at the same time acquisition and
validation of knowledge. Possible applications of the algorithm in domains of
medical diagnosis and information retrieval are sketched
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and improve NLP models. Mappings of an NLP model's representations of and the
brain activities evoked by linguistic input are typically deployed to reveal
this symbiosis. However, two critical problems limit its advancement: 1) The
model's representations (artificial neurons, ANs) rely on layer-level
embeddings and thus lack fine-granularity; 2) The brain activities (biological
neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e.,
voxel/region) and thus lack integrations and interactions among brain
functions. To address those problems, in this study, we 1) define ANs with
fine-granularity in transformer-based NLP models (BERT in this study) and
measure their temporal activations to input text sequences; 2) define BNs as
functional brain networks (FBNs) extracted from functional magnetic resonance
imaging (fMRI) data to capture functional interactions in the brain; 3) couple
ANs and BNs by maximizing the synchronization of their temporal activations.
Our experimental results demonstrate 1) The activations of ANs and BNs are
significantly synchronized; 2) the ANs carry meaningful linguistic/semantic
information and anchor to their BN signatures; 3) the anchored BNs are
interpretable in a neurolinguistic context. Overall, our study introduces a
novel, general, and effective framework to link transformer-based NLP models
and neural activities in response to language and may provide novel insights
for future studies such as brain-inspired evaluation and development of NLP
models
Self Organizing Maps for Visualization of Categories
Visualization of Wikipedia categories using Self Organizing Maps
shows an overview of categories and their relations, helping to narrow down
search domains. Selecting particular neurons this approach enables retrieval of
conceptually similar categories. Evaluation of neural activations indicates that
they form coherent patterns that may be useful for building user interfaces for
navigation over category structures
Neural overlap of L1 and L2 semantic representations across visual and auditory modalities : a decoding approach/
This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations
Annotating Words Using WordNet Semantic Glosses
An approach to the word sense disambiguation (WSD) relaying on
the WordNet synsets is proposed. The method uses semantically tagged glosses
to perform a process similar to the spreading activation in semantic network,
creating ranking of the most probable meanings for word annotation. Preliminary
evaluation shows quite promising results. Comparison with the state-of-theart
WSD methods indicates that the use of WordNet relations and semantically
tagged glosses should enhance accuracy of word disambiguation methods
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