On Creative Uses of Word Associations

Toivonen, Hannu; Gross, Oskar; Toivanen, Jukka M.; Valitutti, Alessandro

doi:10.1007/978-3-642-33042-1_3

Cited by 2 publications

(10 citation statements)

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“…Making similar assertions to our work, Toivonen et al explored the use of word association algorithms in the production of poetry and creative evaluations [55]. When comparing their results to the RAT, Toivonen et al were able to find that simple algorithms could easily outperform human participants [20].…”

Section: Creative Word Associations For Generative Artsupporting

confidence: 72%

“…The remote associates test, or RAT, presents the participant with three seemingly unrelated cue words and asks that participant to come up with a word that connects all three together. The task is intended to be challenging, but computers find the RAT to be trivial [55]. While Mednick believes that the test can measure a participant's creativity, Runco and Acar argue that associative tests fail to measure originality, a necessity for creativity [2].…”

Section: Word Associations and Creativitymentioning

confidence: 99%

“…The system uses machine learning and various methods of learning to create evocative art that has been showcased in art galleries. Other researchers are using narrative theory as the basis for simple story generation [12] and using data analysis to generate poetry [55].…”

Section: Generative Artmentioning

confidence: 99%

“…We use these generative examples as a basis for understanding how we can see the creative process in terms of algorithms and better our understanding of the requirements of generative art. Both Toivonen's generative poetry [55] and ANGELINA's generative games [11] are starting to make use of word associations in generative art in different domains, and while the goal of this thesis is to bolster an author's creativity we also understand the need for better creative word associations in generative art.…”

Section: Generative Artmentioning

confidence: 99%

“…These approaches assume that a document is "generated" according to some statistical distribution and that two documents can be compared by seeing how likely it is that their BoW were generated by the same function. Comparing the likelihood that two documents were generated by the same function can be done simply using methods like chi-squared [8], but a lot of success has been seen using the log-likelihood ratio (LLR) [15,38,55]. LLR provides a measure of how likely it is for a word, w 0 to appear in a BoW vector alongside another, w 1 , compared to the probability that the words will appear independently.…”

Section: Distribution Modelsmentioning

confidence: 99%

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Data-Driven Creativity Enhancement Through Word Association

Hillen¹

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Writing creative stories from a blank page is a challenging task, particularly in the modern game industry where dozens of writers can contribute to the stories and settings of a constantly evolving artificial world. We introduce a system which recommends interesting, evocative, and thematically coherent words to help creators write thematically connected stories. We combine principles from human creativity enhancement and computational creativity to build a creative assistant based on word association research. We show that careful corpus selection, filtering based on emotional sentiment, and promoting remote associations through paragraph scale segmentation can produce recommendations that promote creative goals better than alternative word association algorithms according to our creative word indicators.iii I would like to begin by thanking my thesis supervisor, Dr. David Mould. His attention to detail, his patience, and his enthusiasm for this work was essential during this research. Regardless of the circumstances, his positive attitude and passion in our meetings would always leave me reinvigorated and ready to get back into the work. I could not have completed this work without his outstanding kindness, advice, patience, and support. His guidance has shaped my way of looking at computer science since my first year as an undergraduate student and has kept me driven to pursue this research.I would like to thank the thesis committee for reviewing my work and providing me with valuable suggestions. Their input has improved this thesis and has helped me improve as a researcher. I would also like to acknowledge Carleton University, the School of Computer Science, and GIGL for their financial support and thank them for all of the amazing opportunities they have afforded me over these years. I would like to thank all of my friends, colleagues, and visiting scholars in the GIGL lab for their advice on this thesis, on research, and in academics. iv iv Table of Contents vList of Tables viii List of Figures x4.12 Multi-stimuli recommendations when taking the intersection of TF-IDF weightings using the themes in Table 4.9. Ten recommendations were attempted, but some were unable to find enough recommendations when only looking at the top

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Section: Creative Word Associations For Generative Artsupporting

confidence: 72%

Section: Word Associations and Creativitymentioning

confidence: 99%

Section: Generative Artmentioning

confidence: 99%

Section: Generative Artmentioning

confidence: 99%

Section: Distribution Modelsmentioning

confidence: 99%

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Data-Driven Creativity Enhancement Through Word Association

Hillen¹

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Exploiting Wikipedia Semantics for Computing Word Associations

Jabeen¹

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Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations.

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On Creative Uses of Word Associations

Cited by 2 publications

References 12 publications

Data-Driven Creativity Enhancement Through Word Association

Data-Driven Creativity Enhancement Through Word Association

Exploiting Wikipedia Semantics for Computing Word Associations

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