WordNet

Miller, George A.

doi:10.1145/219717.219748

Cited by 10,351 publications

(3,812 citation statements)

References 4 publications

Supporting

Mentioning

3,741

Contrasting

Unclassified

Order By: Relevance

“…Linguists have tried to characterize the meaning of a word with featurebased approaches, such as semantic roles (Kipper et al, 2006), as well as word-relation approaches, such as WordNet (Miller, 1995). Computational linguists have demonstrated that a word's meaning is captured to some extent by the distribution of words and phrases with which it commonly co-occurs (Church and Hanks, 1990).…”

Section: Introductionmentioning

confidence: 99%

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

2011

View full text Add to dashboard Cite

Recent multivariate analyses of fMRI activation have shown that discriminative classifiers such as Support Vector Machines (SVM) are capable of decoding fMRI-sensed neural states associated with the visual presentation of categories of various objects. However, the lack of a generative model of neural activity limits the generality of these discriminative classifiers for understanding the underlying neural representation. In this study, we propose a generative classifier that models the hidden factors that underpin the neural representation of objects, using a multivariate multiple linear regression model. The results indicate that object features derived from an independent behavioral feature norming study can explain a significant portion of the systematic variance in the neural activity observed in an object-contemplation task. Furthermore, the resulting regression model is useful for classifying a previously unseen neural activation vector, indicating that the distributed pattern of neural activities encodes sufficient signal to discriminate differences among stimuli. More importantly, there appears to be a double dissociation between the two classifier approaches and within-versus between-participants generalization. Whereas an SVM-based discriminative classifier achieves the best classification accuracy in within-participants analysis, the generative classifier outperforms an SVM-based model which does not utilize such intermediate representations in between-participants analysis. This pattern of results suggests the SVM-based classifier may be picking up some idiosyncratic patterns that do not generalize well across participants and that good generalization across participants may require broad, large-scale patterns that are used in our set of intermediate semantic features. Finally, this intermediate representation allows us to extrapolate the model of the neural activity to previously unseen words, which cannot be done with a discriminative classifier.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

2011

View full text Add to dashboard Cite

show abstract

“…Figure 3 shows an example of such a word network, for example, "cash dividend", "dividend report", and "market influence" are examples of bi-gram word pairs from a financial news data sample. LLA is related to Latent Semantic Analysis (LSA, Dumais, Furnas, Landauer and Deerwester, 1988), Probabilistic Latent Semantic Analysis (PLSA, Hofmann, 1999), WordNet (Miller, 1995), Automap (CASOS, 2009), and LDA (Blei, Ng and Jordan, 2003). LDA uses a bag of single words (e.g., associations are computed at the word level) to extract concepts and topics.…”

Section: Machine Vision and Llamentioning

confidence: 99%

Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)

Zhao

Kendall

Johnson

2016

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

View full text Add to dashboard Cite

Abstract:Accurate combat identification (CID) enables warfighters to locate and identify critical airborne objects as friendly, hostile or neutral with high precision. The current CID processes include processing and analysing data from a vast network of sensors, platforms, and decision makers. CID plays an important role in generating the Common Tactical Air Picture (CTAP) which provides situational awareness to air warfare decision-makers. The Big "CID" Data and complexity of the problem pose challenges as well as opportunities. In this paper, we discuss CTAP and CID challenges and some Big Data and Deep Analytics solutions to address these challenges. We present a use case using a unique deep learning method, Lexical Link Analysis (LLA), which is able to associate heterogeneous data sources for object recognition and anomaly detection, both of which are critical for CTAP and CID applications.

show abstract

“…For defining word associations and finding related expressions, we use the well-known WordNet [30] lexical database containing a hierarchical structure of syntactically related nouns, verbs, adjectives, and adverbs grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. We also use statistical NGram models trained with the Brown Corpus [16] to define word probabilities and co-occurrences.…”

Section: Lexical Resourcesmentioning

confidence: 99%

“…• The WordNet Expert generates synonyms, hypernyms, and antonyms for nouns and adjectives based on the WordNet lexical resource [30].…”

Section: Expertsmentioning

confidence: 99%

A Blackboard System for Generating Poetry

Misztal-Radecka

Indurkhya

2016

csci

View full text Add to dashboard Cite

We present a system to generate poems based on the information extracted from input text such as blog posts. Our design uses the blackboard architecture, in which independent specialized modules cooperate during the generation process by sharing a common workspace known as the blackboard. Each module is responsible for a particular task while generating poetry. Our implementation incorporates modules that retrieve information from the input text, generate new ideas, or select the best partial solutions. These distinct modules (experts) are implemented as diverse computational units that make use of lexical resources, grammar models, sentiment-analyzing tools, and languageprocessing algorithms. A control module is responsible for scheduling actions on the blackboard. We argue that the blackboard architecture is a promising way of simulating creative processes because of its flexibility and compliance with the Global Workspace Theory of mind. The main contribution of this work is the design and prototype implementation of an extensible platform for a poetry-generating system that may be further extended by incorporating new experts as well as some existing poetrygenerating systems as parts of the blackboard architecture. We claim that this design provides a powerful tool for combining many of the existing efforts in the domain of automatic poetry generation.

show abstract

WordNet

Cited by 10,351 publications

References 4 publications

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)

A Blackboard System for Generating Poetry

Contact Info

Product

Resources

About