When I build statistical language models (e.g., bigrams and trigrams) trained on a particular corpus or some set of documents, firstly I feel like taking a look at some statistical properties of the set, such as the total number of tokens, the average number of tokens per sentence or per utterance, and so on. Any set of documents, even one consisting of a tremendous number of newspaper articles, is biased in some manner from a statistical perspective, mainly because people collect the data in a particular domain or domains.
Suppose that I need a list of unique words from English sentences described in a text file sentences.txt. Then my initial step is often to use a crude shell command like this:
$ cat sentences.txt | tr ' ' '\n' | sed '/^$/ d' | sort | uniq > unique_words.txt
The list I obtain with this command is neither tokenized nor lemmatized, but it could be sufficient for a quick analysis where I try to get a handle on approximately how many unique words occur in the target document.
If I need a more complicated analysis like extracting a list of unique words with their frequencies, the next step is likely to involve tokenization. A range of tokenization algorithms have so far been proposed according to respective natural languages. As for English, it seems that the simplest way is to build a tokenizer with regular expressions, as mentioned in Jurafsky and Martin 2000. For future reference, I will attach my Java code for English tokenization to this post.
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;
import java.util.regex.Matcher;
/**
* Tokenizes strings described in English.
*
* @author Jun Araki
*/
public class EnglishTokenizer {
/** A string to be tokenized. */
private String str;
/** Tokens. */
private ArrayList<String> tokenList;
/** A regular expression for letters and numbers. */
private static final String regexLetterNumber = "[a-zA-Z0-9]";
/** A regular expression for non-letters and non-numbers. */
private static final String regexNotLetterNumber = "[^a-zA-Z0-9]";
/** A regular expression for separators. */
private static final String regexSeparator = "[\\?!()\";/\\|`]";
/** A regular expression for separators. */
private static final String regexClitics =
"'|:|-|'S|'D|'M|'LL|'RE|'VE|N'T|'s|'d|'m|'ll|'re|'ve|n't";
/** Abbreviations. */
private static final List<String> abbrList =
Arrays.asList("Co.", "Corp.", "vs.", "e.g.", "etc.", "ex.", "cf.",
"eg.", "Jan.", "Feb.", "Mar.", "Apr.", "Jun.", "Jul.", "Aug.",
"Sept.", "Oct.", "Nov.", "Dec.", "jan.", "feb.", "mar.",
"apr.", "jun.", "jul.", "aug.", "sept.", "oct.", "nov.",
"dec.", "ed.", "eds.", "repr.", "trans.", "vol.", "vols.",
"rev.", "est.", "b.", "m.", "bur.", "d.", "r.", "M.", "Dept.",
"MM.", "U.", "Mr.", "Jr.", "Ms.", "Mme.", "Mrs.", "Dr.",
"Ph.D.");
/**
* Constructs a string to be tokenized and an empty list for tokens.
*
* @param str a string to be tokenized
*/
public EnglishTokenizer(String str) {
this.str = str;
tokenList = new ArrayList<String>();
}
/**
* Tokenizes a string using the algorithms by Grefenstette (1999) and
* Palmer (2000).
*/
public void tokenize() {
// Changes tabs into spaces.
str = str.replaceAll("\\t", " ");
// Puts blanks around unambiguous separators.
str = str.replaceAll("(" + regexSeparator + ")", " $1 ");
// Puts blanks around commas that are not inside numbers.
str = str.replaceAll("([^0-9]),", "$1 , ");
str = str.replaceAll(",([^0-9])", " , $1");
// Distinguishes single quotes from apstrophes by segmenting off
// single quotes not preceded by letters.
str = str.replaceAll("^(')", "$1 ");
str = str.replaceAll("(" + regexNotLetterNumber + ")'", "$1 '");
// Segments off unambiguous word-final clitics and punctuations.
str = str.replaceAll("(" + regexClitics + ")$", " $1");
str = str.replaceAll(
"(" + regexClitics + ")(" + regexNotLetterNumber + ")",
" $1 $2");
// Deals with periods.
String[] words = str.trim().split("\\s+");
Pattern p1 = Pattern.compile(".*" + regexLetterNumber + "\\.");
Pattern p2 = Pattern.compile(
"^([A-Za-z]\\.([A-Za-z]\\.)+|[A-Z][bcdfghj-nptvxz]+\\.)$");
for (String word : words) {
Matcher m1 = p1.matcher(word);
Matcher m2 = p2.matcher(word);
if (m1.matches() && !abbrList.contains(word) && !m2.matches()) {
// Segments off the period.
tokenList.add(word.substring(0, word.length() - 1));
tokenList.add(word.substring(word.length() - 1));
} else {
tokenList.add(word);
}
}
}
/**
* Returns tokenized strings.
*
* @return a list of tokenized strings
*/
public String[] getTokens() {
String[] tokens = new String[tokenList.size()];
tokenList.toArray(tokens);
return tokens;
}
}
References:
Daniel Jurafsky and James H. Martin. 2000. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (Prentice Hall Series in Artificial Intelligence). Prentice Hall.
Gregory Grefenstette. 1999. Tokenization. In van Halteren, H. (Ed.), Syntactic Wordclass Tagging. Kluwer.
David D. Palmer. 2000. Tokenisation and sentence segmentation. In Dale, R., Moisl, H., and Somers, H. L. (Eds.), Handbook of Natural Language Processing. Marcel Dekker.