Procesarea limbajului natural

Instalarea NLTK

Pentru instalarea NLTK vom folosi pip.

Scrieti in linia de comanda pip install nltk

Dupa instalare putem importa modulul in consola: import nltk

Descarcati resursele pentru nltk (ideal ar fi pachetele pentru limba engleza + datele din book - sunt niste resurse folosite in manualul principal despre NLTK, dar putem sa le folosim si noi in aplicatii).

Observati ca sunt mai multe taburi:

• Collections - este cu colectiile mari de pachete (un fel de afisare compacta)
• Corpora - este afisarea detaliata a pachetelor cu corpusuri - din care alegeti voi ce aveti nevoie
• Models (date)
• All packages - pachetele afisate detaliat.

Texte predefinite

In exercitiile de laborator vom folosi textele din modulul book (si alte cateva tool-uri de acolo). Pentru a le importa, scriem: >>> from nltk.book import *
*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908
>>> Ar trebui sa vedeti un output asemanator cu cel de mai sus.

Analiza morfologica

Pentru a realiza analiza morfologica, vom folosi POS taggerul (Part Of ) de la Stanford: https://nlp.stanford.edu/software/. Indicat este sa descarcati versiunea full. Dezarhivati taggerul si observati un jar cu numele de forma stanford-postagger.jar.

Importati in consola taggerul: from nltk.tag.stanford import StanfordPOSTagger

Vrem sa ne creem o instanta a taggerului: tagger=StanfordPOSTagger(cale_model, cale_jar_tagger)

Pentru a aplica tagurile POS folosim metoda tag()

In cazul in care primiti o eroare la incercarea folosirii taggerului, urmati indicatiile de aici: https://stackoverflow.com/questions/34692987/cant-make-stanford-pos-tagger-working-in-nltk

Incercati sa parsati o propozitie si urmariti semnificatia tagurilor In cazul in care primim un text ca sir si nu ca lista de cuvinte, putem sa il tokenizam: >>> nltk.word_tokenize("It was late. I saw a cat chasing a mouse.")
['It', 'was', 'late', '.', 'I', 'saw', 'a', 'cat', 'chasing', 'a', 'mouse', '.']
>>> Stopwords: >>> from nltk.corpus import stopwords
>>> stopwords.words('english')
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]
>>>

Tema