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#   Copyright 2015
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#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
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#       http://www.apache.org/licenses/LICENSE-2.0
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#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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Prerequisities

  • Java v.8
  • (optional) bash v.4

How to install

Install for command line usage

  • Download latest version from the releases site https://github.com/tudarmstadt-lt/seg/releases
  • unpack into a directory of choice: tar -xzvf lt.seg-version-dist.tar.gz -C <your-preferred-directory>
  • executables can be found in the bin directory, e.g. bin/seg. You can execute it from any directory.
  • (optional) to access the seg binary from anywhere you can add /bin to PATH: export PATH=<your-preferred-directory>:$PATH or symlink seg into a directory which is already in your PATH

Install for programmatic usage

Build with maven:

	git clone https://github.com/tudarmstadt-lt/seg
	cd seg; mvn package -D skipTests

You can now find the jar here and you can add it as a library to your project if your project doesn't use maven:

seg/lt.seg/target/lt.seg-0.7.1-jar-with-dependencies.jar

If you want to use it in a maven project:

	mvn install -D skipTests

and add this as dependency to your pom.xml:

	<groupId>de.tudarmstadt</groupId>
	<artifactId>lt.seg</artifactId>
	<version>0.7.1</version>

Note: the following description is for unix based systems, you cannot run the startup shell scripts on MS Windows, consider using cygwin or run the java commands manually

How to use the lt.seg segmenter

basic usage is as simple as

	cat text.txt | seg > segmented_text.txt

or

	seg < text.txt > segmented_text.txt

or

	seg -f text.txt > segmented_text.txt

lt.seg comes with a number of parameters, run seg -? to get a list of options

Note: for MS Windows based systems replace seg with the correct java command, e.g. java -cp lt.seg-<version>-with-dependencies.jar de.tudarmstadt.lt.seg.app.Segmenter <options>

Options:

  • --sentencesplitter <class> (-s): Sepcify the sentence splitter class. Supported values are:
    • RuleSplitter (default): Applies (language-specific) rules and heuristics to decide when a sentence ends and a new sentence begins
    • BreakSplitter: Java sentence breakiterator instance
    • LineSplitter: Start a new sentence segment only when a new line occurs (supported line separators: \n, \r\n)
    • NullSplitter: Convenience splitter, returns the complete input as one segment
  • --tokenizer <class> (-t) Sepcify the tokenizer class. Supported values are:
    • RuleTokenizer (default): Applies tokenization according to a ruleset specified by the --token-ruleset option parameter
    • DiffTokenizer: Applies simple rules based on the change on Unicode category of consecutive characters
    • BreakTokenizer: Java word breakiterator instance
    • EmptySpaceTokenizer: creates a new segment only when empty spaces are found (supported empty spaces include but are not limited to: <blank>, <protected-blank>, \t, \n, \r, \f, ...)
    • NullTokenizer: Convenience tokenizer, returns the complete input as one segment
  • --normalize <level> (-nl)
    • 0 (default): no normalization, each segment will be printed as it is in the input
    • 1: reduce same consecutive non-word characters, e.g. multiple consecutive blanks will be merged to one. Example: "\t\t\n\t\t" -> "\t\n\t"
    • 2: 1 + replace empty space and punctuation characters with its symbol
    • 3: 2 + replace consecutive numbers and digits within words and number segments themselves with the symbol 0. Example 'He11o World. I am Johnny 5.' -> 'He0o World . I am Johnny 0 .'
    • 4: 3 + replace all non-word segments with its symbol.
    • 5: 4 + lowercase words.
  • --filter <level> (-fl): Note: examples below use normalization level (-nl) 2 and DiffTokenizer
    • 0: no filtering, each segment will be printed separated by blanks (this also includes emptyspace segments, in most cases you probably want to use at least 1 or 2)
    • 1: filter control character segments
    • 2: (default): 1 + filter emptyspace segments
    • 3: 2 + filter unclassified and non-readable segments (attention: results heavily depend on tokenizer)
    • 4: 3 + filter punctuation characters
    • 5: 4 + filter meta data like URLs, file descriptors, emails, wiki markup, emoticons, etc.
    • 6: 4 + filter numbers and words with numbers. Example: "The number is 534 423 or 43. ? :-/ " -> "The number is or" (Only useful with proper token normalization level.)
  • --merge [<level>] (-ml): Note: examples below use normalization level (-nl) 2
    • 0: no merging (default when not specified)
    • 1: merge same consecutive token types if they are not words or words with numbers (default when just -ml specified). Example: "The number is 534 423 or 43. ? :-/ " -> "The number is 0 or 0 . "
    • 2: merge same consecutive words. Example: "Let's go to New New York." -> "Let s go to New York"
  • --sentence-separator <SEP> (-seps): Specify the separator string SEP for sentences (default: '\n').
  • --token-separator <SEP> (-sept): Specify the separator string SEP for tokens (default: ' ').
  • --source-separator <SEP> (-sepd): Specify the separator string SEP for the source description (default: '\t').
  • --onedocperline (-l): Assume one document per line. Also inserts sentence break at line endings.
  • --parallel <num>: run in parallel mode. Note: parallelism > 1 can only be applied if -l is passed. Also note that the order of input and output sentences can not be guaranteed
  • --debug: Enable debugging output.

Apply segmentation programatically:

programmatic usage is as simple as

	import de.tudarmstadt.lt.seg.*;
	
	new DiffTokenizer().init("This is your text you want to tokenize.").forEach(System.out::println)
	
	new RuleSplitter().init("This is your text you want to split into sentences.").forEach(System.out::println)

or

	new DiffTokenizer().init(new InputStreamReader(new FileInputStream("your-file"))).forEach(System.out::println)

	new RuleSplitter().init(new InputStreamReader(new FileInputStream("your-file"))).forEach(System.out::println)

more complex filtering, normalization and only printing when tokens start with the character a:

	new DiffTokenizer().init(...).filteredAndNormalizedTokens(5, 4).forEach(x -> {if(x.startsWith("a")) System.out.println(x);}));

or with sentence splitting before tokenizing:

	ITokenizer tokenizer = new DiffTokenizer();
	new RuleSplitter().init(new BufferedReader(new InputStreamReader(new FileInputStream("your-file")))).forEach(s ->
		tokenizer.init(s.asSTring()).filteredAndNormalizedTokens(5, 4, false, false).forEach(x -> {if(x.startsWith("a")) System.out.println(x);}));

Note: The supplied Tokenizer and SentenceSplitter are not thread safe, each thread needs its own Tokenizer and SentenceSplitter.