tf-idf | 易学教程

Do I use the same Tfidf vocabulary in k-fold cross_validation

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问题 I am doing text classification based on TF-IDF Vector Space Model.I have only no more than 3000 samples.For the fair evaluation, I'm evaluating the classifier using 5-fold cross-validation.But what confuses me is that whether it is necessary to rebuild the TF-IDF Vector Space Model in each fold cross-validation. Namely, would I need to rebuild the vocabulary and recalculate the IDF value in vocabulary in each fold cross-validation? Currently I'm doing TF-IDF tranforming based on scikit-learn

How to implement TF_IDF feature weighting with Naive Bayes

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问题 I'm trying to implement the naive Bayes classifier for sentiment analysis. I plan to use the TF-IDF weighting measure. I'm just a little stuck now. NB generally uses the word(feature) frequency to find the maximum likelihood. So how do I introduce the TF-IDF weighting measure in naive Bayes? 回答1: You can visit the following blog shows in detail how do you calculate TFIDF. 回答2: You use the TF-IDF weights as features/predictors in your statistical model. I suggest to use either gensim [1]or

How to implement TF_IDF feature weighting with Naive Bayes

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自然语言处理入门

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自然语言处理分类自然语言理解是个综合的系统工程，涉及了很多细分的学科。代表声音的音系学：语言中发音的系统化组织。代表构词法的词态学：研究单词构成以及相互之间的关系。代表语句结构的句法学：给定文本的那部分是语法正确的。代表理解的语义句法学和语用学：给定文本的含义和目的是什么。语言理解涉及语言、语境和各种语言形式的学科。但总的来说，自然语言理解又可以分为三个方面：词义分析句法分析语义分析自然语言的生成则是从结构化的数据（可以通俗理解为自然语言理解分析后的数据）以读取的方式自动生成文本。主要有三个阶段：文本规划：完成结构化数据中的基础内容规划。语句规划：从结构化数据中组合语句来表达信息流。实现：产生语法通顺的语句来表达文本。中文文本分类做一个中文文本分类任务，首先要做的是文本的预处理，对文本进行分词和去停用词操作，来把字符串分割成词与词组合而成的字符串集合并去掉其中的一些非关键词汇（像是：的、地、得等）。再就是对预处理过后的文本进行特征提取。最后将提取到的特征送进分类器进行训练。研究与应用 NLP 在现在大火的 AI 领域有着十分丰富的应用。总体来说，自然语言处理的研究问题（主要）有下面几种：信息检索：对大规模文档进行索引。语音识别：识别包含口语在内的自然语言的声学信号转换成符合预期的信号。机器翻译：将一种语言翻译成另外一种语言。

TF-IDF文本向量化

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1.文本数据的向量化 1.1名词解释 CF：文档集的频率，是指词在文档集中出现的次数 DF：文档频率，是指出现词的文档数 IDF：逆文档频率，idf = log(N/(1+df))，N为所有文档的数目，为了兼容df=0情况，将分母弄成1+df。 TF：词在文档中的频率 TF-IDF：TF-IDF= TF*IDF 1.2文本数据样本集为了讲解文本数据的向量化，假设我们有4个文本，所有文本一共有6个不同的词，如下所示。 1.3计算汇总 1.4代码实现 # -*- coding: utf-8 -*- """ Author:蔚蓝的天空tom Talk is cheap, show me the code Aim:实现文本型数据的TF-IDF向量化 """ import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer def sklearn_tfidf(): tag_list = ['iphone guuci huawei watch huawei', 'huawei watch iphone watch iphone guuci', 'skirt skirt skirt

How do i visualize data points of tf-idf vectors for kmeans clustering?

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问题 I have a list of documents and the tf-idf score for each unique word in the entire corpus. How do I visualize that on a 2-d plot to give me a gauge of how many clusters I will need to run k-means? Here is my code: sentence_list=["Hi how are you", "Good morning" ...] vectorizer=TfidfVectorizer(min_df=1, stop_words='english', decode_error='ignore') vectorized=vectorizer.fit_transform(sentence_list) num_samples, num_features=vectorized.shape print "num_samples: %d, num_features: %d" %(num

SMOTE initialisation expects n_neighbors <= n_samples, but n_samples < n_neighbors

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问题 I have already pre-cleaned the data, and below shows the format of the top 4 rows: [IN] df.head() [OUT] Year cleaned 0 1909 acquaint hous receiv follow letter clerk crown... 1 1909 ask secretari state war whether issu statement... 2 1909 i beg present petit sign upward motor car driv... 3 1909 i desir ask secretari state war second lieuten... 4 1909 ask secretari state war whether would introduc... I have called train_test_split() as follows: [IN] X_train, X_test, y_train, y_test = train_test

SMOTE initialisation expects n_neighbors <= n_samples, but n_samples < n_neighbors

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Lucene custom scoring for numeric fields

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问题 I would like to have, in addition to standard term search with tf-idf similarity over text content field, scoring based on "similarity" of numeric fields. This similarity will be depending on distance between the value in query and in document (e.g. gaussian with m= [user input], s= 0.5) I.e. let's say documents represent people, and person document have two fields: description (full text) age (numeric). I want to find documents like description:(x y z) age:30 but age to be not the filter ,

Calculating tf-idf among documents using python 2.7

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问题 I have a scenario where i have retreived information/raw data from the internet and placed them into their respective json or .txt files. From there on i would like to calculate the frequecies of each term in each document and their cosine similarity by using tf-idf. For example: there are 50 different documents/texts files that consists 5000 words/strings each i would like to take the first word from the first document/text and compare all the total 250000 words find its frequencies then do