표제어 추출 실습

실습 연습

 

표제어 추출 함수인 words_lemmatizer() 함수를 만들어 주세요.

• words_lemmatizer() 함수는 (단어, 품사) 형태로 품사 태깅이 된 리스트를 파라미터로 받고, 표제어 추출을 한 결과를 반환합니다.
• Penn Treebank POS Tag를 WordNet POS Tag로 바꾸기 위한 함수 penn_to_wn()을 미리 만들어 놨습니다. preprocess.py에서 해당 함수를 불러와 사용할 수 있습니다.
• WordNet POS Tag의 품사와 매칭되지 않는 경우 표제어 추출을 하지 않은 원래 단어를 결과에 추가합니다.
• penn_to_wn() 함수를 사용해서 품사를 WordNet POS Tag로 바꿔 주세요.

 
lemmatization_practice.py

import nltk
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet as wn
from processing import pos_tagger
from processing import penn_to_wn
from text import TEXT
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

corpus = TEXT
tokenized_sents = sent_tokenize(corpus)
pos_tagged_words = pos_tagger(tokenized_sents)

lemmatizer = WordNetLemmatizer()

# 표제어 추출 함수
def words_lemmatizer(pos_tagged_words):
    lemmatized_words = []
    
    # 여기에 코드를 작성하세요.
    for word, tag in pos_tagged_words:
        wn_tag = penn_to_wn(tag)
        
        if wn_tag in (wn.NOUN, wn.ADJ, wn.ADV, wn.VERB):
            lemmatized_words.append(lemmatizer.lemmatize(word, wn_tag))
        else:
            lemmatized_words.append(word)
            
    return lemmatized_words
    
words_lemmatizer(pos_tagged_words)

processing.py

import nltk
import pandas as pd
from nltk.tokenize import word_tokenize
from collections import Counter
from text import TEXT
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize
from nltk.tag import pos_tag
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet as wn
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')


# 등장 빈도 기준 정제 함수
def clean_by_freq(tokenized_words, cut_off_count):
    # 파이썬의 Counter 모듈을 통해 단어의 빈도수 카운트하여 단어 집합 생성
    vocab = Counter(tokenized_words)
    
    # 빈도수가 cut_off_count 이하인 단어 set 추출
    uncommon_words = {key for key, value in vocab.items() if value <= cut_off_count}
    
    # uncommon_words에 포함되지 않는 단어 리스트 생성
    cleaned_words = [word for word in tokenized_words if word not in uncommon_words]

    return cleaned_words

# 단어 길이 기준 정제 함수
def clean_by_len(tokenized_words, cut_off_length):
    # 길이가 cut_off_length 이하인 단어 제거
    cleaned_by_freq_len = []
    
    for word in tokenized_words:
        if len(word) > cut_off_length:
            cleaned_by_freq_len.append(word)

    return cleaned_by_freq_len

# 불용어 제거 함수
def clean_by_stopwords(tokenized_words, stop_words_set):
    cleaned_words = []
    
    for word in tokenized_words:
        if word not in stop_words_set:
            cleaned_words.append(word)
            
    return cleaned_words

# 포터 스테머 어간 추출 함수
def stemming_by_porter(tokenized_words):
    porter_stemmer = PorterStemmer()
    porter_stemmed_words = []

    for word in tokenized_words:
        stem = porter_stemmer.stem(word)
        porter_stemmed_words.append(stem)

    return porter_stemmed_words

# 품사 태깅 함수
def pos_tagger(tokenized_sents):
    pos_tagged_words = []

    for sentence in tokenized_sents:
        # 단어 토큰화
        tokenized_words = word_tokenize(sentence)
    
        # 품사 태깅
        pos_tagged = pos_tag(tokenized_words)
        pos_tagged_words.extend(pos_tagged)
    
    return pos_tagged_words

# Penn Treebank POS Tag를 WordNet POS Tag의 4가지 태그로 손쉽게 바꾸기
def penn_to_wn(tag):
    if tag.startswith('J'):
        return wn.ADJ
    elif tag.startswith('N'):
        return wn.NOUN
    elif tag.startswith('R'):
        return wn.ADV
    elif tag.startswith('V'):
        return wn.VERB

# 표제어 추출
def words_lemmatizer(pos_tagged_words):
    lemmatizer = WordNetLemmatizer()
    lemmatized_words = []

    for word, tag in pos_tagged_words:
        wn_tag = penn_to_wn(tag)

        if wn_tag in (wn.NOUN, wn.ADJ, wn.ADV, wn.VERB):
            lemmatized_words.append(lemmatizer.lemmatize(word, wn_tag))
        else:
            lemmatized_words.append(word)

    return lemmatized_words

text.py

TEXT = """Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, 'and what is the use of a book,' thought Alice 'without pictures or conversation?'
So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her.
There was nothing so very remarkable in that; nor did Alice think it so very much out of the way to hear the Rabbit say to itself, 'Oh dear! Oh dear! I shall be late!' (when she thought it over afterwards, it occurred to her that she ought to have wondered at this, but at the time it all seemed quite natural); but when the Rabbit actually took a watch out of its waistcoat-pocket, and looked at it, and then hurried on, Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it, and fortunately was just in time to see it pop down a large rabbit-hole under the hedge.
In another moment down went Alice after it, never once considering how in the world she was to get out again.
The rabbit-hole went straight on like a tunnel for some way, and then dipped suddenly down, so suddenly that Alice had not a moment to think about stopping herself before she found herself falling down a very deep well.
Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled 'ORANGE MARMALADE', but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody, so managed to put it into one of the cupboards as she fell past it.
"""

실행결과

출처 코드잇