User:Cristinac/Day2

CLiPS Computational Linguistics & Psycholinguistics

Guy de Pauw, Walter Daelemans, Tom De Smedt

-Chomsky’s work on linguistics: language acquisition device. -extracting knowledge from data

Natural language processing most information is in unstructured data (text) data in digital form big data (too big to handke with conventional means)

>90% of currently available data was created in the last 2 years

Problems: accuracy levels, speed, fundamental problems (form-meaning relation, semantics, world knowledge)

1 objective knowledge (machine reading): recognising which word types are used 2 subjective knowledge: sentiment, opinion, emotion, modality, (un)certainty. especially with the advent of social media 3 meta knowledge: authorship, author attributes (educational level,age,gender,personality,region,illness), text attributes (date of writing,..): of the, on the, has been tf IDF

https://en.wikipedia.org/wiki/Tf%E2%80%93idf

Schwartz HA Eichstaedt JC 2013 Personality, Gender, and Language in the age of Social Media - controversial http://www.ppc.sas.upenn.edu/socialmediapub.pdf

women using more pronouns, men use more determiners and quant ors -relational language - women -informative language - men

Lexical / morphological, syntactic reasons for bad translations ambiguity. example: snappy little girl’s school, adding only in a sentence, all students know two languages (the same languages?) paraphrase. inference. ex. The mayors prohibited the students to demonstrate because they preached the revolution (who is they?)

Language processing pipeline: text input to meaning output Deep Understanding text input —> tokenization/normalization lemmatization (reducing words to their meaning in the dictionary) part-of-speech tagging: determining what meaning/types of words they are shallow parsing (who is doing what to whom) modality/negation word sense disambiguation (ex bank) semantic role labelling named-entity recognition co-reference resolution —>meaning output

Text mining (shallow understanding) contents: extract facts (concepts and relations between concepts) and opinions meta-data

Text mining (Marti Hearst 2003) Don Swanson 1981: medical hypothesis generation

example: deception

not everyone is equally successful in deceiving others

Liars use, fewer exclusive words, fewer self and other references, fewer time related words, fewer tentative words, more space related words, more motion verbs, more negations, more negative and positive emotions

Cornell University study (Ott et al 2011)

Human judges fail to make the distinction (truth bias), low inter annotator agreement, 2 out of 3 perform at chance level, classifier succeeds (90% accurate). cues: more superlatives, deceptive: imaginative rather than informative language however. there are more than 1 differentiations between the text sources

Text categorisation Documents —> Documents—>classes —>text classifier

                                                                           —>linguistically analysed data    —>text classifier

looking at what defines an author, and not what he writes about Hans Moravec diagram

Sentiment mining sentiment lexicon, classifiers and annotation

politiekebarometer.be www.clips.uantwerpen.be/cqrellations

SAS, R, Python http://www.clips.ua.ac.be/pages/pattern

from pattern.web import Twitter from pattern.en import sentiment

for tweet in Twitter(language=“en”).search(“#obama”): print tweet.text csp files data sheet functionality of pattern

Classifiers: ex: predict the sentiment polarity, predict the position of a face

training document, class event, bag-of-words word bigrams, character trigrams. example: wrong spelling of “exellent”: exe, xel, ell, len, ent word lemmas tokenization: Goed!=goed+!

Annotation=gold standard

online learning option

annotation process

concept clusters

Deep Learning Based on neural networks encode world knowledge into our vocabulary queen=king-man+woman

word2vec (not deep learning, same principle, similar additional increase) - application applications: language technology, speech technology, image recognition, recommender systemen

RUBENS CODE

CODE 1: from pattern.web import PDF from pattern.en import sentiment, parse from pattern.db import Datasheet

ds = Datasheet()

f = open('Bible.pdf') pdf = PDF(f) ds.append((pdf.string, 'Bible'))

f = open('quran.pdf') pdf = PDF(f) ds.append((pdf.string, 'Quran'))

ds.save('bible_quran.csv')

print 'saved!'

CODE 2: from pattern.web import URL, plaintext from pattern.vector import Document, NB, KNN, SLP, SVM, POLYNOMIAL from pattern.db import csv from pattern.en import parse import math

1. classifier = SVM(kernel=POLYNOMIAL, degree=10)

classifier = SVM()

print 'TRAINING:' for text, book in csv('bible_quran_torah.csv'):

       length = len(text)
       # part_len = int(math.floor(length/10))
       # print book
       # # print part_len
       # for i in xrange(1,10):
       #         print i
       #         s = text[i*part_len : i*part_len + part_len]
       #         v = Document(parse(s, tokenize=True, lemata=True, tags=False, relations=False, chunks=False), type=book, stopwords=True)
       #         classifier.train(v)
       
       v = Document(parse(text, tokenize=True, lemata=True, tags=False, relations=False, chunks=False), type=book, stopwords=True)
       classifier.train(v)

print 'CLASSES:',classifier.classes

print 'RESULTS\n======'

return_discrete = True

print "OBAMA" s = open("speech_obama.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "OSAMA" s = open("speech_osama.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "MALCOLM X" s = open("speech_malcolmx").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "ANITA" s = open("essay_anita.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "POPE" s = open("speech_pope.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "NETANYAHU" s = open("speech_netanyahu.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "LUTHER KING" s = open("speech_luther-king.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)

print "CQRRELATIONS" s = open("cqrrelations.txt").read().replace('\n',) s = parse(plaintext(s), tokenize=True, lemata=True, tags=False, relations=False, chunks=False) print classifier.classify(Document(s), discrete=return_discrete)