tokenizer 0.1.12

A tokenizer for Icelandic text

Overview

Tokenization is a necessary first step in many natural language processing tasks, such as word counting, parsing, spell checking, corpus generation, and statistical analysis of text.

Tokenizer is a compact pure-Python (2 and 3) module for tokenizing Icelandic text. It converts Python text strings to streams of token objects, where each token object is a separate word, punctuation sign, number/amount, date, e-mail, URL/URI, etc. It also segments the token stream into sentences, considering corner cases such as abbreviations and dates in the middle of sentences.

The package contains a dictionary of common Icelandic abbreviations, in the file src/tokenizer/Abbrev.conf.

Tokenizer is derived from a corresponding module in the Greynir project (GitHub repository here), by the same author. Note that Tokenizer is licensed under the MIT license while Greynir is licensed under GPLv3.

To install:

pip install tokenizer

To use (for Python 3, you can omit the u"" string prefix):

from tokenizer import tokenize, TOK

text = (u"Málinu var vísað til stjórnskipunar- og eftirlitsnefndar "
        u"skv. 3. gr. XVII. kafla laga nr. 10/2007 þann 3. janúar 2010.")

for token in tokenize(text):

        print(u"{0}: '{1}' {2}".format(
                TOK.descr[token.kind],
                token.txt or "-",
                token.val or ""))

Output:

S_BEGIN: '-' (0, None)
WORD: 'Málinu'
WORD: 'var'
WORD: 'vísað'
WORD: 'til'
WORD: 'stjórnskipunar- og eftirlitsnefndar'
WORD: 'skv.' samkvæmt
ORDINAL: '3.' 3
WORD: 'gr.' grein
ORDINAL: 'XVII.' 17
WORD: 'kafla'
WORD: 'laga'
WORD: 'nr.' númer
NUMBER: '10' (10, None, None)
PUNCTUATION: '/' 4
YEAR: '2007' 2007
WORD: 'þann'
DATE: '3. janúar 2010' (2010, 1, 3)
PUNCTUATION: '.' 3
S_END: '-'

Note the following:

• Sentences are delimited by TOK.S_BEGIN and TOK.S_END tokens.

• Composite words, such as stjórnskipunar- og eftirlitsnefndar, are coalesced into one token.

• Well-known abbreviations are recognized and their full expansion is available in the token.val field.

• Ordinal numbers (3., XVII.) are recognized and their value (3, 17) is available in the token.val field.

• Dates, years and times are recognized and the respective year, month, day, hour, minute and second values are included as a tuple in token.val.

• Numbers, both integer and real, are recognized and their value is available in the token.val field.

The tokenize() function

To tokenize a text string, call tokenizer.tokenize(text). This function returns a Python generator of token objects. Each token object is a simple namedtuple with three fields: (kind, txt, val) (see below).

The tokenizer.tokenize() function is typically called in a for loop:

for token in tokenizer.tokenize(mystring):
        kind, txt, val = token
        if kind == tokenizer.TOK.WORD:
                # Do something with word tokens
                pass
        else:
                # Do something else
                pass

Alternatively, create a token list from the returned generator:

token_list = list(tokenizer.tokenize(mystring))

The token object

Each token is represented by a namedtuple with three fields: (kind, txt, val).

The kind field contains one of the following integer constants, defined within the TOK class:

PUNCTUATION = 1
TIME = 2
DATE = 3
YEAR = 4
NUMBER = 5
WORD = 6
TELNO = 7
PERCENT = 8
URL = 9
ORDINAL = 10
TIMESTAMP = 11
CURRENCY = 12       # Not used
AMOUNT = 13
PERSON = 14         # Not used
EMAIL = 15
ENTITY = 16         # Not used
UNKNOWN = 17

S_BEGIN = 11001     # Sentence begin
S_END = 11002       # Sentence end

To obtain a descriptive text for a token kind, use TOK.descr[token.kind] (see example above).

The txt field contains the original source text for the token.

In the case of abbreviations that end a sentence, the final period ‘.’ is a separate token, and it is consequently omitted from the abbreviation token’s txt field. A sentence ending in o.s.frv. will thus end with two tokens, the next-to-last one being the tuple (TOK.WORD, "o.s.frv", "og svo framvegis") - note the omitted period in the txt field - and the last one being (TOK.PUNCTUATION, ".", 3) (the 3 is explained below).

The val field contains auxiliary information, corresponding to the token kind, as follows:

• For TOK.PUNCTUATION, the val field specifies the whitespace normally found around the symbol in question:

  TP_LEFT = 1   # Whitespace to the left
  TP_CENTER = 2 # Whitespace to the left and right
  TP_RIGHT = 3  # Whitespace to the right
  TP_NONE = 4   # No whitespace

• For TOK.TIME, the val field contains an (hour, minute, second) tuple.

• For TOK.DATE, the val field contains a (year, month, day) tuple (all 1-based).

• For TOK.YEAR, the val field contains the year as an integer.

• For TOK.NUMBER, the val field contains a tuple (number, None, None). (The two empty fields are included for compatibility with Greynir.)

• For TOK.WORD, the val field contains the full expansion of an abbreviation, or None if the word is not abbreviated.

• For TOK.PERCENT, the val field contains a tuple of (percentage, None, None).

• For TOK.ORDINAL, the val field contains the ordinal value as an integer.

• For TOK.TIMESTAMP, the val field contains a (year, month, day, hour, minute, second) tuple.

• For TOK.AMOUNT, the val field contains an (amount, currency, None, None) tuple. The amount is a float, and the currency is an ISO currency code, i.e. “USD” for dollars ($ sign) or “EUR” for euros (€ sign). (The two empty fields are included for compatibility with Greynir.)

The correct_spaces() function

Tokenizer also contains the utility function tokenizer.correct_spaces(text). This function returns a string after splitting it up and re-joining it with correct whitespace around punctuation tokens. Example:

>>> tokenizer.correct_spaces("Frétt \n  dagsins:Jón\t ,Friðgeir og Páll ! 100  /  2  =   50")
'Frétt dagsins: Jón, Friðgeir og Páll! 100/2 = 50'

The Abbrev.conf file

Abbreviations recognized by Tokenizer are defined in the Abbrev.conf file, found in the src/tokenizer/ directory. This is a text file with abbreviations, their definitions and explanatory comments. The file is loaded into memory during the first call to tokenizer.tokenize() within a process.