NLP里面的一些基本概念
1，corpus 语料库
a computer-readable collection of text or speech
2，utterance 发音
比如下面一句话：I do uh main- mainly business data processing
uh 是 fillers，填充词（Words like uh and um are called fillers or filled pauses ）。The broken-off word main- is fragment called a fragment
3，Types are the number of distinct words in a corpus
给你一句话，这句话里面有多少个单词呢？ 标点符号算不算单词？有相同lemma的单词算不算重复的单词？比如“he is a boy and you are a girl”，这句话中 “is”和 "are"的lemma 都是 be。另外，这句话中 "a" 出现了两次。那这句话有多少个单词？这就要看具体的统计单词个数的方式了。
Tokens are the total number N of running words.
4，Morphemes
A Morpheme is the smallest division of text that has meaning. Prefxes and suffxes are examples of morphemes
These are the smallest units of a word that is meaningful. 比如说：“bounded”，"bound"就是一个 morpheme，而Morphemes而包含了后缀 ed
5，Lemma（词根） 和 Wordform（词形）
Cat 和 cats 属于相同的词根，但是却是不同的词形。
Lemma 和 stem 有着相似的意思：
6，stem
Stemming is the process of finding the word stem of a word 。比如，walking 、walked、walks 有着相同的stem，即： walk
与stem相关的一个概念叫做 lemmatization，它用来确定一个词的基本形式，这个过程叫做lemma。比如，单词operating，它的stem是 ope，它的lemma是operate
Lemmatization is a more refined process than stemming and uses vocabulary and morphological techniques to find a lemma. This can result in more precise analysis in some situations 。
The lemmatization process determines the lemma of a word. A lemma can be thought of as the dictionary form of a word.
（Lemmatization 要比 stemming 复杂，但是它们都是为了寻找 单词的 “根”）。但是Lemmatization 更复杂，它用到了一些词义分析(finding the morphological or vocabulary meaning of a token)
Stemming and lemmatization: These processes will alter the words to get to their "roots". Similar to stemming is Lemmatization. This is the process of fnding its lemma, its form as found in a dictionary.
Stemming is frequently viewed as a more primitive technique, where the attempt to get to the "root" of a word involves cutting off parts of the beginning and/or ending of a token.
Lemmatization can be thought of as a more sophisticated approach where effort is devoted to finding the morphological or vocabulary meaning of a token。
比如说 having 的 stem 是 hav，但是它的 lemma 是have
再比如说 was 和 been 有着不同的 stem，但是有着相同的 lemma : be
7，affix 词缀 （prefix 和 suffxes）
比如说：一个单词的 现在进行时，要加ing，那么 ing 就是一个后缀。
This precedes or follows the root of a word . 比如说，ation 就是 单词graduation的后缀。
8,tokenization （分词）
就是把一篇文章拆分成一个个的单词。The process of breaking text apart is called tokenization
9，Delimiters （分隔符）
要把一个句子 分割成一个个的单词，就需要分隔符，常用的分隔符有：空格、tab键(\t)；还有 逗号、句号……这个要视具体的处理任务而定。
The elements of the text that determine where elements should be split are called Delimiters 。
10，categorization （归类）
把一篇文本，提取中心词，进行归类，来说明这篇文章讲了什么东西。比如写了一篇blog，需要将这篇blog的个人分类，方便以后查找。
This is the process of assigning some text element into one of the several possible groups.
11,stopwords
某些NLP任务需要将一些常出现的“无意义”的词去掉，比如：统计一篇文章频率最高的100个词，可能会有大量的“is”、"a"、"the" 这类词，它们就是 stopwords。
Commonly used words might not be important for some NLP tasks such as general searches. These common words are called stopwords
由于大部分文本都会包含 stopwords，因此文本分类时，最好去掉stopwords。关于stopwords的一篇参考文章。
12，Normalization （归一化）
将一系列的单词 转化成 某种 统一 的形式，比如：将一句话的各个单词中，有大写、有小写，将之统一转成 小写。再比如，一句话中，有些单词是 缩写词，将之统一转换成全名。
Normalization is a process that converts a list of words to a more uniform sequence.
Normalization operations can include the following:（常用的归一化操作有如下几种）
converting characters to lowercase（大小写转换）,expanding abbreviation（缩略词变成全名）, removing stopwords（移除一些常见的“虚词”）, stemming, and lemmatization.（词干或者词根提取）
参考资料
《JAVA自然语言处理》Natural Language processing with java
