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Celebrate Flowers and Invitations 26057 Bouquet Canyon Road Santa Clarita, CA 91350
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Mon-Fri: 9:00am to 6:00pm

Saturday: 9:00am to 4:00pm

26057 Bouquet Canyon Road Santa Clarita, CA, 91350

myra@flowersandinvitations.com

Celebrate – Planning for an Event to Remember was established in 1991 by Myra Harbour in an effort to bring a chic, premium floral shop to the Santa Clarita Valley. Over twenty years later, Celebrate has gained numerous awards and recognition from the community, and the Press, for our one of a kind creations and individually tailored service. We offer exquisite floral and plant designs, daily flower delivery service to Henry Mayo Hospital and Eternal Valley Cemetery. “We look forward to Celebrating with you!” -Myra Harbour and the Staff at Celebrate Voted Best Florist in Santa Clarita

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I ordered this online from the UK for a friend in Santa Clarita. Ordering online was quick and easy to use. My friend loved the flower arrangement and sent me a picture and it looked exactly like the one on the website. I was very pleased with my purchase.

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Topic models (and LDA is one kind of topic modeling algorithm) are generative, unsupervised methods of discovering latent patterns in large collections of natural language text: generative because topic models produce new data that describe the corpora without altering it; unsupervised because the algorithm uses a form of probability rather than metadata to create the model; and latent patterns because the tests are not looking for top-down structural features but instead use word-by-word calculations to discover trends in language. David Blei, credited with developing LDA and probabilistic topic modeling methods, describes topic models the following way:

Topic models have been developed with information engineering applications in mind. As a statistical model, however, topic models should be able to tell us something, or help us form a hypothesis, about the data. What can we learn about the language (and other data) based on the topic model posterior? (Blei “Introduction” 84)

Blei stages topic modeling as an ex post facto method for challenging our assumptions about natural language data. In other words, once a collection has been created, LDA can test our assumptions about what topics are discoverable.

What drew me to LDA as a tool for discovering latent patterns of language use in ekphrastic poetry was that it seemed particularly well-suited to identifying the tropes of ekphrastic discourse. One could reasonably expect that since the language of stillness, breathlessness, desire, and competition are commonly found in ekphrastic poetry, that LDA might be able to locate ekphrastic poems within a much larger corpus — in this case 4,500 poems. I wondered, could topic models detect gendered language, tropes, or the language of stillness in ways that “we can learn ” about the genre more broadly? This is the question that began Revising Ekphrasis , a digital topic modeling and corpus discovery project I developed that uses digital and computational tools to explore ekphrastic and non-ekphrastic poetry.

The topic model represented in this article is one of several from the Revising Ekphrasis project . I’ve chosen this particular model to focus on for two reasons. It was the first model in the project to produce results that prompted a reconsideration of the tropes and conventions of ekphrasis. Secondly, it illustrates how figurative language resists thematic topic assignments and by doing so, effectively increases the attractiveness of topic modeling as a methodological tool for literary analysis of poetic texts. Few questions will find “answers” here. Instead the hope is to uncover new methods for addressing enduring humanities questions that we might fruitfully ask about figurative language with LDA.

A form of text mining developed in response to the growing challenge of managing, organizing, and navigating large, digitized document archives, topic modeling was developed with primarily non-fiction corpora in mind. One of the most notable, early uses of LDA by Blei explores a digitized archive of the journal Science . Other exemplary topic modeling projects have used Wikipedia, NIH grants, JSTOR, and an archive of Classics journals. [ 3 ] As literary scholars well know, however, poems exercise language in ways purposefully inverse to other forms of writing, such as journal articles, encyclopedia entries, textbooks, and newspaper articles. Consequently, it is reasonable to predict that there will be differences between topics created by LDA models of poetry and models of non-fiction texts. In terms of the non-figurative language found in topic models of the journal Science , Blei explains that topics detect thematic trends across texts:

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