tf-idf in Barok 2014
rching the web. It is a mechanism not
dissimilar to thought process involved in retrieving particular information
online. And search engines have it built in their indexing algorithms as well.
There is a paper proposing attaching words generated by tf-idf to the
hyperlinks when referring websites 14(http://bscit.berkeley.edu/cgi-
bin/pl_dochome?query_src=&format=html&collection=Wilensky_papers&id=3&show_doc=yes).
This would enable finding the referred content even after the link is dead.
Hyperlink
this feature and it can be easily
tested: 15(http://www.cs.berkeley.edu/~phelps/papers/dissertation-
abstract.html?lexical-
signature=notemarks+multivalent+semantically+franca+stylized).
There is another measure, cosine similarity, which takes tf-idf further and
can be applied for clustering texts according to similarities in their
specificity. This might be interesting as a feature for digital libraries, or
even a way of organising library bottom-up into novel categories, new
discourses could em
xts which are interesting enough to serve as a ground to discuss
and experiment with dictionaries, classifications, indexes, and tools for
references retrieval. These all belong to the poetic devices of knowledge-
making.
4
Command-line example of tf-idf and concordance in 3 steps.
* 1\. Process the files text.1-5.txt and produce freq.1-5.txt with lists of (nonlemmatized) words (in respective texts), ordered by frequency:
> for i in {1..5}; do tr '[A-Z]' '[a-z]' < text.$i.txt | tr -c '[a-z]'
'[\
Display 200 300 400 500 600 700 800 900 1000 ALL characters around the word.