As It can be noticed I’am doing some research on different AI topics, I’m just reading a bunch of global information for curiosity, AI algorithms have always facinated me.
This is a good tutorial on Neural Networks that can be found in Spanish.
Another page I found is this , it is also an introduction to different kinds of NN’s.
There is a nice API for bloglines web services written in Python, it is called, pybloglines and can be obtained from here.
There are also some good examples. I’ll use this API to extract new unread posts from a Bloglines account and then store each post with its contents into an XML file for further processing.
The goal is to obtain posts ( with contents ) that I can date and later process. The idea is, instead of writting my own robot to get posts from several places, I’ll use the Bloglines API, thus saving a lot of time.
Universal Feed Parser is needed to run pybloglines.
I’ve been reading this introduction to PyXml. The install process is really easy, for Gentoo there is already an ebuild, for MacOsX, it has to be downloaded and then just run: python setup.py install , that’s it.
import xml.dom.minidom
doc = xml.dom.minidom.Document()
#Create an element, it’ll be root element
elem = doc.createElementNS (“http://”,”rootElem”)
#append the child to the doc
doc.appendChild(elem)
#Create element named fill
elemfill = doc.createElement (“fill”)
#Append it to root child
elem.appendChild (elemfill)
#Create text element
text = doc.createTextNode(“Text node inside fill”)
#Append it to elemfill
elemfill.appendChild (text)
#create another element named filldos
elemfill = doc.createElement (“filldos”)
#text element
text = doc.createTextNode(“Text node inside fill dos”)
#append filldos to root element
elem.appendChild (elemfill)
#append text to filldos
elemfill.appendChild (text)
#print all root elements
for elem in doc.childNodes:
print elem.localName
print doc.toxml()
This test just creates some sample XML and prints it out. In this example XML I haven’t used any attributes, but I’ll just post later how to use them, the code would have been too long to post it. The PyXml API seems quite easy to learn. I’ll just keep posting notes about it as I go through it.
I ‘ve just started reading this article . I think the base of searching is understanding the queries and the content in which you are looking for. The article gives an insight view to a Corpus-based aproach to semantic interpretation in NLP , it’s worth reading it.
Definition of a corpus:
from Corpus Linguistics
A collection of texts, spoken and/or written, which has been designed and compiled based on a set of clearly defined criteria.
CORPUS[13c: from Latin corpus body. The plural is usually corpora].
A collection of texts, especially if complete and self-contained: the corpus of Anglo-Saxon verse. Plural also corpuses. In linguistics and lexicography, a body of texts, utterances, or other specimens considered more or less representative of a language, and usually stored as an electronic database. Currently, computer corpora may store many millions of running words, whose features can be analysed by means of tagging (the addition of identifying and classifying tags to words and other formations) and the use of concordancing programs. Corpus linguistics studies data in any such corpus.
(The Oxford Companion to the English Language, ed. McArthur & McArthur, 1992)
References:
Links to anything & everything to do with the use of language corpora.
For quite a long time I wanted to try the livehtttpsheaders extension. This extensions lets you edit http headers and queries on-line. It’s really useful when debugging Web applications, as you can even modify the post data.
I found this extension even more useful when debugging Https connections as you can see and edit all HTTPS transactions.
Livehttpsheaders can be obtained from : Mozilla Developers: Http Live Headers