commit | 7035d2e4dab6e2e3d697f59de17e056e045e25c6 | [log] [tgz] |
---|---|---|
author | Akron <nils@diewald-online.de> | Thu Oct 28 00:54:01 2021 +0200 |
committer | Akron <nils@diewald-online.de> | Thu Oct 28 00:54:01 2021 +0200 |
tree | 845147a8d241771c9fd7fcb25e5a3043f09f50a4 | |
parent | 96fdc9be352df941e7304b2e5ceeb8e2078d7307 [diff] |
Fix sentence_pos handling Change-Id: Ib9f5c0bf8bb06d53dec72437c7f337c749658605
Implementation of a finite state automaton for natural language tokenization, based on a finite state transducer generated with Foma.
The library contains sources for a german tokenizer based on KorAP-Tokenizer.
$ echo "Es war spät, schon ca. 2 Uhr. ;-)" | datok tokenize -t testdata/tokenizer.matok Es war spät , schon ca. 2 Uhr . ;-)
The special END OF TRANSMISSION
character (\x04
) can be used to mark the end of a text.
Caution: When experimenting with STDIN this way, you may need to disable history expansion.
The FST generated by Foma must adhere to the following rules, to be converted by Datok:
@_TOKEN_SYMBOL_@
, that denotes the end of a token.@_TOKEN_SYMBOL_@
.@_TOKEN_SYMBOL_@
s mark a sentence end.@_TOKEN_SYMBOL_@
marks the end of a token instead.A minimal usable tokenizer written in XFST and following the guidelines to tokenizers in Beesley and Karttunen (2003) and Beesley (2004) would look like this:
define TE "@_TOKEN_SYMBOL_@"; define WS [" "|"\u000a"|"\u0009"]; define PUNCT ["."|"?"|"!"]; define Char \[WS|PUNCT]; define Word Char+; ! Compose token ends define Tokenizer [[Word|PUNCT] @-> ... TE] .o. ! Compose Whitespace ignorance [WS+ @-> 0] .o. ! Compose sentence ends [[PUNCT+] @-> ... TE \/ TE _ ]; read regex Tokenizer;
Hint: For development it's easier to replace @_TOKEN_SYMBOL_@
with a newline.
To build the tokenizer tool, run
$ go build ./cmd/datok.go
To create a foma file from the example sources, first install Foma, then run in the root directory of this repository
$ cd src && \ foma -e "source tokenizer.xfst" \ -e "save stack ../mytokenizer.fst" -q -s && \ cd ..
This will load and compile tokenizer.xfst
and will save the compiled FST as mytokenizer.fst
in the root directory.
To generate a Datok FSA (matrix representation) based on this FST, run
$ datok convert -i mytokenizer.fst -o mytokenizer.datok
To generate a Datok FSA (double array representation*) based on this FST, run
$ datok convert -i mytokenizer.fst -o mytokenizer.datok -d
The final datok file can then be used as a model for the tokenizer.
Internally the FSA is represented either as a matrix or as a double array.
Both representations mark all non-word-character targets with a leading bit. The transduction is greedy with a single backtracking option to the last ε (aka tokenend) transition.
The double array representation (Aoe 1989) of all transitions in the FST is implemented as an extended DFA following Mizobuchi et al. (2000) and implementation details following Kanda et al. (2018).
Datok is published under the Apache 2.0 License.
The german tokenizer shipped is based on work done by the Lucene project (published under the Apache License), David Hall (published under the Apache License), Çağrı Çöltekin (published under the MIT License), and Marc Kupietz (published under the Apache License).
The foma parser is based on foma2js, written by Mans Hulden (published under the Apache License).
Aoe, Jun-ichi (1989): An Efficient Digital Search Algorithm by Using a Double-Array Structure. IEEE Transactions on Software Engineering, 15 (9), pp. 1066-1077.
Beesley, Kenneth R. & Lauri Karttunen (2003): Finite State Morphology. Stanford, CA: CSLI Publications.
Beesley, Kenneth R. (2004): Tokenizing Transducers. https://web.stanford.edu/~laurik/fsmbook/clarifications/tokfst.html
Hulden, Mans (2009): Foma: a finite-state compiler and library. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 29-32.
Mizobuchi, Shoji, Toru Sumitomo, Masao Fuketa & Jun-ichi Aoe (2000): An efficient representation for implementing finite state machines based on the double-array. Information Sciences 129, pp. 119-139.
Kanda, Shunsuke, Yuma Fujita, Kazuhiro Morita & Masao Fuketa (2018): Practical rearrangement methods for dynamic double-array dictionaries. Software: Practice and Experience (SPE), 48(1), pp. 65–83.