ConceptNet — A Practical Commonsense Reasoning Tool-Kit

Liu, H.; Singh, Push

doi:10.1023/b:bttj.0000047600.45421.6d

Cited by 1,230 publications

(856 citation statements)

References 25 publications

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“…Contextual disambiguation is also central to one of the best established and most influential theories of human word and sentence comprehension (Landauer and Dumais, 1997), and to ConceptNet, which is a software package for natural language processing that achieves success in many textual reasoning tasks by applying context-oriented inference to a large real-world knowledge base (Liu and Singh, 2004).…”

Section: Contextual Disambiguation In Perceptionmentioning

confidence: 99%

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

Phillips

Clark

Silverstein

2015

Neuroscience & Biobehavioral Reviews

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A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities.

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Section: Contextual Disambiguation In Perceptionmentioning

confidence: 99%

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

Phillips

Clark

Silverstein

2015

Neuroscience & Biobehavioral Reviews

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“…To decrease the annotation cost, cartoon image should be described with less keywords. By utilizing ontology like ConceptNet [11] or LifeNet [12], the annotation cost can be decreased. The approach with ontology can be also useful in selecting appropriate cartoon images and cartoon stories with landmark events.…”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Funny Cartoons Diary for Everyday Mobile Life

Song

Jung

Cho

2006

Lecture Notes in Computer Science

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Abstract. The notable developments in pervasive and wireless technology enable us to collect enormous sensor data from each individual. With contextaware technologies, these data can be summarized into context data which support each individual's reflection process of one's own memory and communication process between the individuals. To improve reflection and communication, this paper proposes an automatic cartoon generation method for fun. Cartoon is a suitable medium for the reflection and the communication of one's own memory, especially for the emotional part. By considering the fun when generating cartoons, the advantage of the cartoon can be boosted. For the funnier cartoon, diversity and consistency are considered during the cartoon generation. For the automated generation of diverse and consistent cartoon, context data which represent the user's behavioral and mental status are exploited. From these context information and predefined user profile, the similarity between context and cartoon image is calculated. The cartoon image with high similarity is selected to be merged into cartoon cuts. Selected cartoon cuts are arranged with the constraints for the consistency of cartoon story. To evaluate the diversity and consistency of the proposed method, several operational examples are employed.

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“…The developers of ConceptNet (Havasi et al 2007, Liu andSingh 2004) structured the sentences in OMCS into a more structured, machine-friendly format. The ConceptNet3 database currently contains over 700,000 assertions, and is still being maintained and improved.…”

Section: Sentiment Analysis and Affective Sensingmentioning

confidence: 99%