Difference between revisions of "Settles, CALC 2010"

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'''Titular'''
 
'''Titular'''
Available here: [http://muse.fawm.org/titular]
 
  
This tool makes use of a template-based approach to synthesis titles. Table 1 shows some sample templates that were induced As opposed to using hand-crafted templates, Titular learns using the following:
+
Available online here: [http://muse.fawm.org/titular]
 +
 
 +
This tool makes use of a template-based approach to synthesis titles. Table 1 shows some sample templates that were induced by Titual. As opposed to using hand-crafted templates, Titular learns using the following:
 
* Titles in training corpus are tokenized and POS tagged
 
* Titles in training corpus are tokenized and POS tagged
 
* Open word classes are substitued with their POS tag, and close words remain in tact
 
* Open word classes are substitued with their POS tag, and close words remain in tact
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'''LyriCloud'''
 
'''LyriCloud'''
Available here: [http://muse.fawm.org/lyricloud]
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 +
Available online here: [http://muse.fawm.org/lyricloud]
 +
 
 +
Figure 2 shows some example output by LyriCloud.
 +
 
 +
[[File:settles-calc-2010-2.png]]
  
 
== Related papers ==
 
== Related papers ==

Revision as of 02:22, 31 March 2011

Citation

Computational Creativity Tools for Songwriters. Burr Settles. In Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity, 2010.

Online version

From Author's Webpage

Summary

This paper describes two natural language processing based computational creativity tools to assist songwriters, originally deployed to aid songwriters participating in FAWM, a social challenge website devoted to writing songs in short period of time. The two tools described are Titular, a text synthesis algorithm for generating new song titles, and LyriCloud, which is a word-level explorer that allow users interactively select words and receive suggestions for lyrical words.

Titular

Available online here: [1]

This tool makes use of a template-based approach to synthesis titles. Table 1 shows some sample templates that were induced by Titual. As opposed to using hand-crafted templates, Titular learns using the following:

  • Titles in training corpus are tokenized and POS tagged
  • Open word classes are substitued with their POS tag, and close words remain in tact
  • Thresholds are set to on templates to remove junk/infrequent templates

Settles-calc-2010.png

LyriCloud

Available online here: [2]

Figure 2 shows some example output by LyriCloud.

Settles-calc-2010-2.png

Related papers