《NLP with Python》学习笔记(一)

文章目录

  • 绘制带条件的频率分布表
  • 评估词汇多样性
  • 分词
    • 自定义函数实现分词
    • 使用模拟退火算法的非确定搜索
  • 常用的基础函数

绘制带条件的频率分布表

#布朗语料库
import nltk
from nltk.corpus import brown
brown.categories()
#带条件的频率分布函数
cfd = nltk.ConditionalFreqDist(
    (genre,word)
    for genre in brown.categories()
    for word in brown.words(categories=genre)
)
genres = brown.categories()
cfd.tabulate(conditions = genres, samples=modals)

《NLP with Python》学习笔记(一)_第1张图片
《NLP with Python》学习笔记(一)_第2张图片

评估词汇多样性

#词汇多样性
def lexical_diversity(text):
    return len(text)/len(set(text))
def percentage(count,total):
    return 100 * count / total
lexical_diversity(text4)
percentage(text4.count("a"),len(text4))

在这里插入图片描述

分词

自定义函数实现分词

def segment(text,segs):
    words = []
    last = 0
    for i in range(len(segs)):
        if segs[i] == "1":
            words.append(text[last:i+1])
            last = i+1
    words.append(text[last:])
    return words
text = "doyouseethekittyseethedoggydoyoulikethekittylikethedoggy"
seg1 = "000000000000000100000000001000000000000000010000000000001"
seg2 = "01001001001000010010010000101001000100100001000100100001"
seg1_out = segment(text,seg1)
seg2_out = segment(text,seg2)
def evaluate(text,segs):
    words = segment(text,segs)
    text_size = len(words)
    lexicon_size = len(" ".join(list(set(words))))
    return text_size + lexicon_size

《NLP with Python》学习笔记(一)_第3张图片

使用模拟退火算法的非确定搜索

from random import randint
def flip(segs, pos):
    return segs[:pos] + str(1-int(segs[pos])) + segs[pos+1:]
def flip_n(segs, n):
    for i in range(n):
        segs = flip(segs, randint(0, len(segs)-1))
    return segs
def anneal(text, segs, iterations, cooling_rate):
    temperature = float(len(segs))
    while temperature > 0.5:
        best_segs, best = segs, evaluate(text, segs)
        for i in range(iterations):
            guess = flip_n(segs, int(round(temperature)))
            score = evaluate(text, guess)
            if score < best:
                best, best_segs = score, guess
            score, segs = best, best_segs
            temperature = temperature / cooling_rate
            print(evaluate(text,segs),segment(text,segs))
    print(segs)
    return segs

text = "doyouseethekittyseethedoggydoyoulikethekittylikethedoggy"
seg1 = "00000000000000010000000000100000000000000001000000000000"
anneal(text, seg1, 5000, 1.2)

常用的基础函数

import nltk
from nltk.book import *

#concordance()搜索指定单词,连同上下文一起显示
text1.concordance("monstrous")

#可找出与monstrous有相似上下文的词语
text1.similar("monstrous")

《NLP with Python》学习笔记(一)_第4张图片

#可找出两个或两个以上的词共同的上下文
text2.common_contexts(["monstrous","very"])

在这里插入图片描述

你可能感兴趣的