Difference between revisions of "Language Processing for Art"
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Language Processing for Art: Reading, Writing, Listening, Speaking. | Language Processing for Art: Reading, Writing, Listening, Speaking. | ||
| − | This course explores the relationship between text and image, interactivity, and the database/archive from the perspective of contemporary language processing techologies. | + | This course explores the relationship between text and image, interactivity, and the database/archive from the perspective of contemporary language processing techologies. |
We will survey recent developments from the fields of natural language processing (NLP) and computational linguistics (CL) as well as relevant artistic upper-level course exploring natural language processing (NLP) and computational linguistics (CL) as applied to art. It explores questions of the relationship between text and image, interactivity, and the database/archive from the perspective of contemporary language processing technologies. This is an upper-level course, and requires previous programming experience. | We will survey recent developments from the fields of natural language processing (NLP) and computational linguistics (CL) as well as relevant artistic upper-level course exploring natural language processing (NLP) and computational linguistics (CL) as applied to art. It explores questions of the relationship between text and image, interactivity, and the database/archive from the perspective of contemporary language processing technologies. This is an upper-level course, and requires previous programming experience. | ||
| − | + | == resources == | |
| − | + | Python | |
| − | * | + | Natural Language Tool Kit (http://nltk.org) |
| − | + | * NLTK was originally created in 2001 as part of a computational linguistics course in the Department of Computer and Information Science at the University of Pennsylvania. Since then it has been developed and expanded with the help of dozens of contributors. It has now been adopted in courses in dozens of universities, and serves as the basis of many research projects. See Table I.2 for a list of the most important NLTK modules. | |
| + | Notes on the Index. Rosalind Krauss. | ||
| + | |||
| + | CS Pierce. | ||
| + | |||
| + | Database Aesthetics. Lev Manovich. | ||
| + | |||
| + | Semantic Web. XML. metadata. | ||
| + | automatic content extraction. | ||
| + | * Semantic measures: | ||
| + | ** Perl WN-SIMILARITY http://wn-similarity.sourceforge.net/ | ||
* machine reading | * machine reading | ||
** optical character recognition | ** optical character recognition | ||
| Line 15: | Line 25: | ||
** text generation | ** text generation | ||
* listening | * listening | ||
| + | ** speech recognition http://cmusphinx.sourceforge.net/sphinx4/ | ||
** language recognition | ** language recognition | ||
** automatic-phone systems | ** automatic-phone systems | ||
Latest revision as of 09:30, 23 August 2009
Language Processing for Art: Reading, Writing, Listening, Speaking.
This course explores the relationship between text and image, interactivity, and the database/archive from the perspective of contemporary language processing techologies.
We will survey recent developments from the fields of natural language processing (NLP) and computational linguistics (CL) as well as relevant artistic upper-level course exploring natural language processing (NLP) and computational linguistics (CL) as applied to art. It explores questions of the relationship between text and image, interactivity, and the database/archive from the perspective of contemporary language processing technologies. This is an upper-level course, and requires previous programming experience.
resources
Python Natural Language Tool Kit (http://nltk.org)
- NLTK was originally created in 2001 as part of a computational linguistics course in the Department of Computer and Information Science at the University of Pennsylvania. Since then it has been developed and expanded with the help of dozens of contributors. It has now been adopted in courses in dozens of universities, and serves as the basis of many research projects. See Table I.2 for a list of the most important NLTK modules.
Notes on the Index. Rosalind Krauss.
CS Pierce.
Database Aesthetics. Lev Manovich.
Semantic Web. XML. metadata. automatic content extraction.
- Semantic measures:
- Perl WN-SIMILARITY http://wn-similarity.sourceforge.net/
- machine reading
- optical character recognition
- machine writing
- text generation
- listening
- speech recognition http://cmusphinx.sourceforge.net/sphinx4/
- language recognition
- automatic-phone systems
- talking
- speech synthesis
- O.S. built in speech synthesis
- accessibility tools
