Articles tagged with Semantics
A study with 24 participants aged 18-30 used fMRI scans to map brain areas involved in reading written words in two artificial alphabets. The results showed a hierarchy of brain regions, with posterior regions encoding letter position within words and left mid-to-anterior regions processing phonological and semantic information.
Research using functional MRI found that brain activity for spoken and written words is nearly identical, adding to understanding of language processing. The study's results support the idea that the same areas of the brain process semantic information regardless of sense modality.
Researchers developed a machine-learning method to quantify semantic richness of conversational language, predicting psychosis with high accuracy. They analyzed sound-related words and low semantic density, detecting hidden patterns in people's language predictive of psychosis.
MORPHEUS, a new processor architecture, blocks potential attacks by encrypting and randomizing key bits 20 times per second. This makes vulnerabilities virtually impossible to exploit, providing a future-proof secure system.
Researchers at Aalto University studied how listeners immerse themselves in audiobooks using fMRI and word lists. The study found that similar words in a list predicted similarities in brain activity, particularly in language processing and visual cortex areas.
Research reveals distinct brain regions process concrete and abstract word meanings separately, with a network of areas working together in different languages. The findings provide new insights into how our brains represent word-meaning structure.
A new paper proposes a broadly applicable formal definition of semantic information, which emphasizes how information contributes to a system's ability to perpetuate itself. Semantic info is causal necessary for a system to maintain its existence over time.
Researchers have devised a novel strategy to identify people with mild cognitive impairment at risk of developing Alzheimer's disease. The Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L) tool measures specific memory deficits that align with imaging findings for abnormal brain amyloid accumulation.
A Lehigh research team is developing a 'Google for research data' that can assist scientists in locating datasets across various disciplines. The team aims to create a domain-agnostic search engine that utilizes user-centric methods to develop dataset search tools and novel indexing techniques.
Researchers used a newly developed interpretive technique to analyze neural networks trained for machine translation and speech recognition. They found that lower-level tasks, such as sound recognition or part-of-speech recognition, are prioritized before higher-level tasks like transcription or semantic interpretation.
A new analysis concludes that music uses abstract animated components to convey information about an extra-musical reality, similar to sign languages. The study suggests that the diverse inferences drawn from musical sources are combined using iconic rules, creating a 'fictional source' for what is perceived as real.
Machine learning scientists at Disney Research developed a dynamic word embeddings model that uncovers how the meanings of words change over time. The model, which integrates neural networks and statistics used in rocket control systems, detects semantic change throughout history by analyzing semantic vector spaces.
Researchers at Medical University of South Carolina found a significant correlation between microstructural white matter changes and aphasia recovery after speech therapy. The study supports the dual-stream language model and highlights the importance of structural plasticity in semantic skills, leading to improved treatment outcomes.
Researchers at UC Berkeley have mapped over 1,100 years of metaphoric English language to identify patterns in how humans create and understand metaphorical meaning. This study paves the way for future advances in artificial intelligence and natural language processing systems like Siri.
Researchers use machine learning algorithms with brain imaging technology to identify complex thoughts, such as 'The witness shouted during the trial.' The study found that the mind's building blocks for constructing complex thoughts are formed by the brain's various sub-systems and are not word-based. With an accuracy rate of 87%, the...
A study published in NeuroImage found that brain activation patterns are the same when decoding sentences in English and Portuguese. The research team developed a machine-learning algorithm to recognize sentence meaning in Portuguese based on neural activation patterns learned from English, achieving 67% accuracy.
Researchers from HSE Neurolinguistics Laboratory presented their work expanding Alexander Luria's neuroanatomical model of semantic aphasia by adding white matter tracts to it, which connect various regions of the cortex.
The study used fMRI to measure human brain activation and found that it can predict brain activity patterns within 70% accuracy. The researchers developed a novel approach to map semantic characteristics of words and correlated them with neural activity patterns.
Researchers have developed a systematic approach to studying monkey morphology, syntax, and semantics using methods from theoretical titi monkeys linguistics. This allows them to compare one monkey species to another and analyze the meanings of their calls in detail, revealing complex formal properties and syntax.
Researchers Rumiati and Foroni review how food is represented in the brain, exploring theories on sensory-functional and domain-specific categorization. They find that the 'foodstuff' category can help disambiguate among approaches and highlight the importance of variables like transformation and perceived calories.
Psychologists at the University of Texas at Arlington discovered that verbal interaction, not non-verbal cues, is crucial for mutual understanding between strangers. The study shows that exchanging words and asking questions leads to higher levels of common-ground understanding.
Researchers developed SEISE to detect semantic inconsistencies in content, identifying 11,000 infected sites across .edu, .gov, and .mil domains. The technique uses natural language processing to spot differences between expected content and malicious advertising code.
The algorithms enable a computer to act like a human when encountering an unknown word, looking up words in a dictionary and associating them with context. This development paves the way for computers to perform tasks such as translating texts, summarizing articles, and finding similar documents
Researchers create a detailed semantic atlas of the brain's language processing areas, revealing similarities in semantic topography across individuals. The study has potential applications in brain-machine interfaces, decoding difficult-to-speak patients, and translating languages.
A new study maps the human brain's semantic atlas, revealing how different areas respond to words with similar meanings. This discovery holds promise for decoding inner dialogue in individuals who struggle to communicate, such as stroke or ALS patients.
A University of Rochester team has developed a system that outperforms other approaches to creating computer-generated image captions by considering the meaning and context of words, not just images. The winning approach combines top-down and bottom-up methods to create more accurate and coherent captions.
An international team of researchers has revealed that for many universal concepts, the world's languages feature a common structure of semantic relatedness. This study uses a new methodology to measure how closely words' meanings are related within and between languages.
Researchers at MIT develop a new way of doing machine learning that enables semantically related concepts to reinforce each other. In experiments, their system outperformed conventional training methods in predicting Flickr tags and even when considering semantically similar tags.
Dr. Zhiqiang Lin has developed a technique that allows monitoring of virtual machines for invasions and viruses, bridging the semantic gap in data structures. This breakthrough has the potential to improve cloud security and protect online services from malicious intruders.
Researchers Floris Roelofsen and Donka F. Farkas investigate the workings of 'yes' and 'no', finding that their interpretation is affected by intonation and sentence polarity. Their framework explains distribution and interpretation in English and predicts patterns across languages.
Researchers created a detailed brain map of language impairments in aphasia following stroke, identifying four dimensions or factors: semantic recognition, speech recognition, speech production, and semantic errors. The study found that specific brain areas, such as the left Sylvian fissure and white matter bottleneck, are associated w...
The 2014 Semantic Web Challenge was won by three teams: Mining the Web of Linked Data with RapidMiner, Enabling Live Exploration on The Graph of Things, and DIVE into the Event-Based Browsing of Linked Historical Media. The Big Data Track was won by Extending Tables with Data from over a million websites.
The study highlights the importance of understanding how social networks facilitate the spread of misinformation. The researchers propose a taxonomy to analyze and mitigate the impact of semantic attacks on human behavior.
In a new study, researchers found that four-to-seven-year-old children rely on phonological characteristics when assigning nouns to classes, even though semantic features are more predictive. This suggests that children actively filter and select data from their environment to learn language.
Researchers found that jazz musicians' brains use syntax to process music, similar to how spoken language is processed in the brain. This study sheds light on the complex relationship between music and language.
Research reveals that consumers value sales more when considering the original price, and retailers can leverage this by highlighting list prices. In three experiments, authors found that relying on competing product similarities increased consideration of both original and sale prices, while focusing on dissimilarities led to a focus ...
The winners of the 2013 Semantic Web Challenge have successfully combined innovative semantic web technology with an end user practical focus and efficiency in use. This year's challenge saw 17 proposals, with four Open Track Challenge winners and one Big Data Track winner selected by a panel of experts.
New tools developed by MIT researchers allow people with minimal programming skills to rapidly build cellphone applications for disaster relief, utilizing the App Inventor and RDF standards. The apps can monitor multiple data sources, display information on Google maps, and enable users to annotate or supplement the data.
A study by Davide Zoccolan and colleagues found that the anterior inferotemporal cortex (IT) categorizes objects based on visual similarity, except for four-legged animals which are represented by semantic meaning. The IT retains a 'raw' lower-level coding of object properties like color and size.
Researchers investigated brain activation during reading of Chinese characters with and without hand-radicals using fMRI. They found that characters with hand-radicals activated the right medial frontal gyrus, supporting embodied semantics theory.
Researchers at SISSA uncover inconsistencies in the dependence of the linguistic system on the motor one, with damage to specific brain regions affecting gesture imitation but not language processing. The study employs voxel-based lesion symptom mapping to establish a link between brain damage and performance decline.
Brazilian physicists Thiago Silva and Diego Amancio devised a method to disambiguate words with multiple meanings by analyzing their connections in literary classics. Their approach, based on deterministic tourist walks, showed significant accuracy rates comparable to traditional semantic-based methods.
Researchers are working on creating a model that can accurately distinguish word meanings using a weighted map of relationships created from hundreds of thousands of documents. This approach utilizes supercomputers and Hadoop to process large amounts of text data, enabling the development of more effective language technologies.
A new study by MIT researchers shows that people make mental edits when processing confusing information and use specific strategies to make sense of language. They also adapt their approach when presented with increasingly nonsensical sentences, inferring lower amounts of 'noise' in the language.
The study proposes that half of a concept's meaning is a summary of how rewarding and risky an object is. The researchers used internet blogs to estimate the number of good or bad things associated with objects, finding that 'evaluation' and 'activity' dimensions were strong predictors of word usage.
Researchers investigate patterns of dysgraphia in participants with primary progressive aphasia, comparing them to those with stroke-related dysgraphia. They also explore nonverbal semantic processing in individuals with semantic aphasia and dementia, shedding light on brain-behavior relationships.
Researchers found that people with mild cognitive impairment and Alzheimer's disease struggle to process semantic information, leading to broader implications for daily functioning. Semantic processing deficits may be slowed through training, and could contribute to decline in activities of daily living.
Researchers at UC Berkeley have created an interactive map of the brain's visual organization, revealing that thousands of objects and activities are grouped into semantic neighborhoods. The study may help diagnose brain disorders and improve image recognition systems.
A study using electroencephalography found that people's brains often don't notice key words with contradictory meanings, leading to semantic illusions. Shallow processing is more likely under cognitive load or when multitasking.
A new method of social network analysis has been developed to identify opinion leaders and media experts in the field of health. The system, which uses text mining technology, was tested on the topic of obesity and found experts with an accuracy of about 90%. It also identified non-scientific experts who support a particular cause.
Researchers create 'Literal Lucy' to test speakers' ability to distinguish between literal and pragmatic meaning. The study finds that adopting a third-party perspective improves reliability in making this distinction.
Researchers found that metaphorical associations with weight are just as influential as physical weight on consumer judgments. In experiments, participants who were primed to think about weight responded similarly to those who physically carried heavy loads.
New research from Psychological Science shows that attentional task sets modulate subliminal semantic and visuomotor processes differently. High-capacity individuals recover more quickly from distractions than low-capacity individuals.
A study published in Cortex found that the first elements of semantic memory to deteriorate are distinguishing characteristics of a concept, such as a zebra's stripes. This blurs related concepts, leading to temporary improvements in recognizing related words in early Alzheimer's disease.
A new study published in the Journal of Consumer Research found that the order in which food items are considered affects people's calorie estimates. Researchers discovered that switching the order can significantly alter perceived calorie content, even when foods are dissimilar.
Researchers have developed a tool that uses artificial intelligence to provide additional information on complex terms, bridging the semantic gap and improving communication. The approach has been successfully tested in an English composition class, where students could access explanations of teacher-used terminology.
Researchers found that patients with semantic dementia have difficulty identifying individual flavours and assessing the appropriateness of particular flavour combinations. This suggests a 'pan-modal' deficiency of knowledge in semantic dementia, affecting not just words but also common experiences like flavours.
The 2009 Semantic Web Challenge recognized four outstanding applications that demonstrated practical applicability to real-world issues. The winners were awarded cash prizes sponsored by Elsevier, promoting the dissemination of knowledge from academia to society.
The eScience revolution aims to hasten scientific discovery by providing a toolkit for scientists and educators to access and interpret vast amounts of data. Semantic Web technology enables computers to provide meaning to words, linking users to related information and facilitating collaboration between experts and non-experts.
Research reveals that dynamic semantic network of human language is small-world and scale-free but differs from syntactic network in hierarchical structure and node degree correlation. This discovery sheds light on the organization of human semantic knowledge and cognition.