搜索资源列表
WordStem_20121024
- 采用Porter Stemming算法对英文文本进行分词-Preprocessing English text based on Porter Stemming algorithm
Stemmer
- porters algorithm for detecting stemming and stopping words and get rid off it
stemmer
- stemming algorithm for getting the root word of each words. by this can easily compare the different text documents.
previous_process
- 实现英文文档的预处理工作，包括去除停用词和词干提取，本人在vs2008编译通过，包含文档和结果-To English document pre-processing work, including removal of disable word stemming, I vs2008 compiled by contains documentation and results
stemming
- 用java版的stem算法，用于文本去词根用，非常经典的一个算法！-Java version of the stem algorithm for text to root, very classic algorithm!
Stemmer
- Porter Stemmer，英文词根化-Porter Stemmer, English stemming
DFA
- 有限自动机的实现，可用于固定序列匹配，自然语言处理中的词干提取，词缀切分等-The realization of finite automata can be used for fixed sequence matching, natural language processing stemming, affixes cut grading
Porter-Algorith
- 分别用c和java实现词根还原功能 及常用的停用词表-Respectively c and java achieve stemming functions and common stop words table
a
- 用来把英文单词词干化，从而节省内存，减小空间-Used the English word stemming, thus saving memory
stemming_porter
- Program stemming with php
Stems
- Document expresses about text classification and stemming
Stem
- 实现英文文档的分词，并且对词汇进行波特词干处理，输出文章中词干的出现数量-Achieve the English word document and vocabulary Porter Stemming for processing, the output article appeared in the number of stem
R4
- 短文本数据集，各大论文的数据集取材，英文文本，已经stemming,去停词，提炼后的。-R4 short text dataset,english. stemming and non-stop words.
stem
- text file for stemming process for feature selection
mlclass-ex6
- 支持向量机，实现2或多分类，基于matlab仿真，内有说明-ex6.m- Octave scr ipt for the rst half of the exercise ex6data1.mat- Example Dataset 1 ex6data2.mat- Example Dataset 2 ex6data3.mat- Example Dataset 3 svmTrain.m- SVM rraining function svmPredict.m- SVM p
a
- 用来把英文单词词干化，从而节省内存，减小空间-Used the English word stemming, thus saving memory
a
- 用来把英文单词词干化，从而节省内存，减小空间-Used the English word stemming, thus saving memory
2808-14159-1-PB
- In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such as whether to use stemming, term frequency versus binary weighting, negation-enr