Regular Expressions, Text Normalization, and Edit Distance Note (SLP Ch02)

Normalizing text means converting it to a more convenient, standard form.

• tokenization: separating out or tokenizing words from running text
• lemmatization: words have the same root but different surface. Stemming refers to a simpler version of lemmatization in which just strip suffixes from the end of the word.
• sentence segmentation

Regular Expressions

For Re, Please read this note, and exercises in the book can be found here. An important use of regular expressions is in substitutions.

Words

Wheather we consider a token as a word depends on the application.

• Punctuations in pos, parsing, or speech synthesis.
• Disfluencies in speech recognition.
• Capitalized and upcapitalized tokens are different in pos or ner, but same in speech recognition.
• In English, wordforms are sufficient, but in Arabic, is lemmatization.

Types are the number of distinct words, Tokens are the total number of words.

• Herdan’s Law (Heaps’ Law): |V| = kN^β, k and β are positive constants, 0<β<1, β depends on the corpus size and the genre, for large corpora, β ranges from .67 to .75

The number of lemmas can instead of wordform types, dictionaries can help in giving lemma counts.

Corpora

It’s important to consider who produced the language, in what context , for what purpose, and make sure that the models are fit to the data.

• language
• genre
• demographic characteristics of the writer
• time matter

Text Normalization

Word Tokenization and Normalization

Normalization: putting words/tokens in a standard format.

Some examples that keep the punctuation internally:

• abbreviation: m.p.h Ph.D. AT&T cap'n
• special characters and numbers: \$45.55 01/02/06 555,550.50
• urls: http://www.google.com
• tags: #nlproc
• email: someone@gmail.com

A clitic is a part of word that can’t stand on its own, and can only occur when it is attached to another word.

A named entity need to be treated as a single token.

Tokens can also be normalized in which a single normalized form is chosen for words with multiple forms like USA and US.

Penn Treebank tokenization standard can be found here: nltk.tokenize.treebank — NLTK 3.4.1 documentation.

Case folding is another kind of normalization.

• For speech recognition and information retrieval, everything is mapped to lower case
• For sentiment analysis and text classification, IE, MT, case is helpful

Word Segmentation in Chinese: the MaxMatch algorithm

• cannot dealing with unknown words
• genres that differ a lot from the assumptions made by the dictionary builder
• most accurate algorithms generally use statistical sequence models

The code is here: Segmentation

Collapsing words: Lemmatization and Stemming

The most sophisticated methods for lemmatization involve complete morphological parsing of the word.

Morphology is the study of the way words are built up from smaller meaning-bearing units called morphemes. Two broad classes of morphemes can be distinguished:

• stems—the central morpheme of the word, supplying the main meaning

This naive version of morphological analysis is called stemming. One of the most widely used stemming algorithms is the Porter. The algorithm is based on series of rewrite rules run in series, as a cascade, in
which the output of each pass is fed as input to the next pass. Detailed rule lists and code can be found on Porter Stemming Algorithm.

Byte-Pair Encoding

Stemming or lemmatizing can help deal with unknown words.

It uses a different kind of tokenization in which most tokens are words, but some tokens are frequent word parts like -er, so that an unseen word can be represented by combining the parts. The simplest such algorithm is byte-pair encoding, or BPE, which is based on a method for text compression.

Code can be found here.

Sentence Segmentation

Question marks and exclamation points are relatively unambiguous markers of sentence boundaries. Periods, on the other hand, are more ambiguous. The period character “.” is ambiguous between a sentence boundary marker and a marker of abbreviations like Mr. or Inc.

In general, sentence tokenization methods work by building a binary classifier (based on a sequence of rules or on machine learning) that decides if a period is part of the word or is a sentence-boundary marker.

Minimum Edit Distance

Edit distance is a way to quantify string similarity. Minimum edit distance is the minimum number of editing operations needed to transform one string to another. Operation list includes deletion, substitution and insertion.

Levenshtein distance is the simplest weighting factor which has two versions:

• three operations has a cost of 1
• insertion or deletion has a cost of 1, substitution has a cost of 2

The Minimum Edit Distance Algorithm

Given source string X of length n, and target string Y of length m, D(i, j) is the edit distance between X[1…i] and Y[1…j], the edit distance between X and Y is D(n, m).

With a small change, minimum edit distance can also provide the minimum cost alignment between two strings. It’s useful in speech recognition (used to compute the word error rate) and machine translation (plays a role in which sentences in a parallel corpus need to be matched to each other).

The code is here: Edit-Distance.

The Viterbi algorithm is a probabilistic extension of minimum edit distance. Instead of computing the “minimum edit distance” between two strings, Viterbi computes the “maximum probability alignment” of one string with another.

Summary

• Regular expression is powerful for pattern-matching.
• Word tokenization and normalization are generally done by cascades of simple regular expressions substitutions or finite automata.
• Porter algorithm is simple and efficient to do stemming, stripping off affixes. It does not have high accuracy but may be useful for some tasks.
• Minimum edit distance between two strings is the minimum number of operations it takes to edit one to another. It can be computed by dynamic programming, which also results in an alignment of the two strings.