Efficient Sequence Labeling

efselab is a compiler for sequence labeling tools, aimed at producing accurate and very fast part-of-speech (PoS) taggers and named entity recognizers (NER).

To create a PoS tagger, all you need to do is to edit a Python file which specifies which feature templates, tag lexicons and/or word clusters to use. efselab then compiles this specification into C code, which is then compiled into an executable.

The basic algorithm used is simple: a structured perceptron using greedy search for decoding. To maximize performance, all strings are represented as hash sums internally. During decoding, these can then be efficiently combined into feature hashes.

In this way, even rather complex feature templates using word clusters and external lexicons can generate taggers capable of around a million tokens per second.

A detailed description of the algorithms used along with evaluations can be found in the following paper:

• Robert Östling (2018). Part of Speech Tagging: Shallow or Deep Learning?. North European Journal of Language Technology (5), 1--15.

efselab is implemented in Python/C and requires the following software to be installed:

• Python 3 (tested with version 3.4) and setuptools
• gcc (tested with version 4.9) and GNU Make
• Cython (only needed if you want to use the Swedish lemmatizer)

There is no installation as such, all the software is (somewhat inelegantly) contained in the root directory, where configuration files are also assumed to be placed and executed.

First, you need to build the Python module fasthash, which is used to construct lexicon hash tables. Simply type:

make

Each tagger specification file (build_*.py) also functions as a build script. For a complete list of arguments, run e.g.:

python3 build_udt_en.py --help

Then, to build a tagger simply run the corresponding configuration file, the following will build a tagger for the English Universal Dependencies treebank with the name udt_en, both an executable file and a Python module:

python3 build_udt_en.py --name udt_en --python

which will build a tagger for the English part of the Universal Dependencies Treebank. This produces a binary file, udt_en, which contains everything except the model weights. These need to be learned in the following way:

./udt_en train data/udt-en-train.tab data/udt-en-dev.tab udt-en.bin

The final weights are written to the file udt-en.bin, and can the be used for tagging:

./udt_en tag data/udt-en-test.tab udt-en.bin evaluate >/dev/null

Note that the evaluate option requires a tagged input, if you want to tag an untagged file, this can also be done (in this example by stripping off the tags using the cut tool, and using - as the input file to read from stdin):

cut -f 1 data/udt-en-test.tab | ./udt_en tag - udt-en.bin >udt-en-retag.tab

There is a Swedish annotation pipeline, adapted from the Swedish Treebank pipeline (originally using hunpos for POS tagging) created at Uppsala University by Filip Salomonsson. It can do the following:

• Tokenization (using a Python tokenizer by Filip Salomonsson)
• POS tagging (using efselab with a SUC + SIC model)
• Conversion to Universal PoS tags (using efselab trained on Universal Dependencies data plus some postprocessing heuristics by Aaron Smith)
• Lemmatization (using the lexicon-based lemmatizer in lemmatize.pyx)
• Named Entity Recognition (using efselab with a SUC + SIC model)
• Dependency prasing (using MaltParser by Joakim Nivre et al.) into Universal Dependencies format (version 2)

To start using the pipeline, you first need to execute the following convenience script:

scripts/install_swedish_pipeline.sh

Then you should be able to run the pipeline like this:

mkdir output
./swe_pipeline.py -o output --all file.txt

For a more detailed description of the command-line options, run:

./swe_pipeline.py --help

These evaluations are performed with the 17-element PoS tagset from Universal Dependencies version 2. The Swedish model in the leftmost column of the first table uses additional data, all other models use only the training part of the corresponding Universal Dependencies treebank.

The --beam-size argument of the build scripts controls the beam size of the decoder, which is the most important way to balance accuracy and performance. A beam size of 1 is equivalent to a greedy search, which is the fastest option but results in significantly higher error rates than the default beam size (4).

To build a Python module for your tagger, use the --python argument with the configuration script:

python3 build_udt_en.py --name udt_en --python

After this, the tagger can be used from Python in the following way:

>>> import udt_en
>>> with open('udt-en.bin', 'rb') as f: weights = f.read()
...
>>> udt_en.tag(weights, ['A', 'short', 'sentence', '.'])
('DET', 'ADJ', 'NOUN', 'PUNCT')

The second argument can be a tuple or list of either str objects, or a tuple or list containing the values of the different input fields. Using a tuple with a single str object is equivalent to using just the str object.

weights is a bytes object, containing the contents of a model file, i.e. a binary vector of floating-point values.

Users with access to (possibly restricted) training material will likely want to distribute the generated C file and the model file. The end user can then compile the C file for their own platform, and start tagging files.

There are some things to be aware of:

• Tokens are simply truncated at 4095 bytes, don't feed it very long strings!
• Currently only UTF-8 input is supported.

This repository includes a few third-party contributions:

• The English part of the UD Treebank (licensed as CC BY-SA 4.0), see: https://universaldependencies.github.io/docs/
• UTF-8 decoding code from Björn Höhrmann (under the MIT license): http://bjoern.hoehrmann.de/utf-8/decoder/dfa/
• Brown clusters from Turian et al.: http://metaoptimize.com/projects/wordreprs/

Thanks to Emil Stenström for testing and feedback.

The Swedish pipeline wrapper script (including the tokenizer) was originally written by Filip Salomonsson (Uppsala), later modified by Robert Östling (Stockholm/Helsinki) and Aaron Smith (Uppsala). Joakim Nivre and Jesper Näsman (Uppsala) contributed to different parts of the Swedish pipeline.