SECOS - SEmantic COmpound Splitter

SECOS is an unsupervised compound splitter that uses information from a distributional thesaurus (DT). Details about its working can be found in our paper.

Table of Content

• Split compounds
• Apply SECOS to German Compounds
• Apply SECOS to Dutch Compounds
• Decompound text
• Decompound server
• Significance testing
• Precomputed models
• Datasets for Evaluation
• Citation
• License

Split compounds

In order to split compounds SECOS features some parameters and requires a candidate file, which is generated using a distributional thesaurus. In order to use SECOS for splitting German and Dutch compounds, you can use pre-computed candidate lists and the commands shown below.

python decompound_secos.py dt_candidates word_count_file min_word_count(50) file_compound word_index prefix_length(3) suffix_length(3) word_length(5) dash_word(3) upper(upper) epsilon
-----------------------------------------------------
Parameter description:
-----------------------------------------------------
dt_candidates:		file with words and their split candidates, generated from a distributional thesaurus (DT)
word_count_file:	file with word counts used for filtering
min_word_count:		minimal word count used for split candidates (recommended paramater: 50)
file_compound:		file with words that should be decompounded (each compound needs to be in a single line)
word_index:			index of the word in the tab separated file_compound
prefix_length:		length of prefixes that are appended to the right-sided word (recommended parameter: 3)
suffix_length:		length of suffixes that are appended to the left-sided word (recommended parameter: 3)
word_length:		minimal word length that is used from the split candidates (recommended parameter: 5)
dash_word:			heuristic to split words with dash, which has no big impact (recommended: 3)
upper:				consider uppercase letters (=upper) or not (=lower). Should be set for case-sensitive languages e.g. German
epsilon:			smoothing factor (recommended parameter: 0.01

Apply SECOS to German Compounds

In order to compound German words you need to download and unzip the following package: data.zip. Using these files, a list of compounds (german_compounds) can be split using the following command:

python decompound_secos.py data/denews70M_trigram__candidates data/denews70M_trigram__WordCount 50 german_compounds 0 3 3 5 3 upper 0.01 > output

For explaining the output, we also define an examplary german_compounds file with the following content:

Donaudampfschifffahrtsgesellschaftskapitän
Hefeweizenbier

Executing SECOS, we retrieve the file output has the following content:

U Donau-dampf-schifffahrts-gesellschafts-kapitän  U       Donau-dampf-schifffahrts-gesellschafts-kapitän  Donaudampfschifffahrtsgesellschaftskapitän      Donaudampfschifffahrtsgesellschaftskapitän  Donaudampfschifffahrtsgesellschaftskapitän      Donau-dampf-schifffahrts-gesellschafts-kapitän  -1 Donaudampfschifffahrtsgesellschaftskapitän
U Hefe-weizen-bier        U       Hefe-weizen-bier        Hefeweizenbier  Hefeweizenbier  Hefeweizenbier  Hefe-weizen-bier     2   Hefeweizenbier 

with the following columns:

1)  Method that was considered best for the compounding 
    following possibilities: 
        C1: use similar candidate units
        C2: use extended similar candidate units
        C3: use combination of C1 and C2
        U:  Use the generated dictionary method
2)  Splitted word using method described in column 1)
3)  Method that performs the most splits (same abbreviations like 1)
4)  Splitted word using method described in column 3)
5)  Split compound using C1
6)  Split compound using C2
7)  Split compound using C3
8)  Split compound using U
9)  Word frequency of the word considered
10) the line of the input file

Apply SECOS to Dutch Compounds

In order to compound Dutch words you need to download and unzip the following package: data.zip. Using these files, a list of compounds (dutch_compounds) can be split using the following command:

python decompound_secos.py data/dutchCoW_trigram__candidates data/dutchCoW_trigram__WordCount 50 dutch_compounds 0 3 3 5 3 lower 0.01

For more information about input and output read the example on applying SECOS to German.

Training Candidates for New Language

In order to train candidates for a new language, a distributional thesaurus (DT) is required. The DT is a file of 3 tab separated columns with two words and their similarity score. The file needs to be ordered by the first word and the similarity score in descending ordering. The DT is generated using JoBimText (www.jobimtext.org) using only neighboring words by using the Trigram holing operation. Using such a DT the candidates can easily be generated with the following command:

cat dt | python generateDecompoundCandidates.py > dt_candidates

Decompound text

For decompounding from the STDIN using the best option you can use the following python script which has similar parameters as the script above:

echo "Ich esse gerne Zitroneneis" | python decompound_text_secos.py dt_candidates word_count_file min_word_count(50) prefix_length(3) suffix_length(3) word_length(5) dash_word(3) upper(upper) epsilon
-----------------------------------------------------
Parameter description:
-----------------------------------------------------
dt_candidates:      file with words and their split candidates, generated from a distributional thesaurus (DT)
word_count_file:    file with word counts used for filtering
min_word_count:     minimal word count used for split candidates (recommended paramater: 50)
prefix_length:      length of prefixes that are appended to the right-sided word (recommended parameter: 3)
suffix_length:      length of suffixes that are appended to the left-sided word (recommended parameter: 3)
word_length:        minimal word length that is used from the split candidates (recommended parameter: 5)
dash_word:          heuristic to split words with dash, which has no big impact (recommended: 3)
upper:              consider uppercase letters (=upper) or not (=lower). Should be set for case-sensitive languages e.g. German
epsilon:            smoothing factor (recommended parameter: 0.01

Using the German model the following command can be used:

echo "Ich esse gerne Zitroneneis" | python decompound_text_secos.py denews70M_trigram__candidates denews70M_trigram__WordCount 50 3 3 5 3 upper 0.01

Decompound server

When you want to decompound many different documents, the decompounding can take quite some time. In order to reduce the time needed for decompounding, I provide some decompounding server. Thus, the model does not need to be loaded serveral times. In addition, all decompounded words are stored in memory, which speeds up the decompounding of text tremendously. The server can be started with the same parameters as the 'Decompound text' and has an additional parameter for the port the server should run.

python decompound_server.py dt_candidates word_count_file min_word_count(50) prefix_length(3) suffix_length(3) word_length(5) dash_word(3) upper(upper) epsilon port
-----------------------------------------------------
Parameter description:
-----------------------------------------------------
dt_candidates:          file with words and their split candidates, generated from a distributional thesaurus (DT)
word_count_file:        file with word counts used for filtering
min_word_count:         minimal word count used for split candidates (recommended paramater: 50)
prefix_length:          length of prefixes that are appended to the right-sided word (recommended parameter: 3)
suffix_length:          length of suffixes that are appended to the left-sided word (recommended parameter: 3)
word_length:            minimal word length that is used from the split candidates (recommended parameter: 5)
dash_word:              heuristic to split words with dash, which has no big impact (recommended: 3)
upper:                  consider uppercase letters (=upper) or not (=lower). Should be set for case-sensitive languages e.g. German
epsilon:                smoothing factor (recommended parameter: 0.01

Using the German model the server can be started as follows:

python decompound_server.py denews70M_trigram__candidates denews70M_trigram__WordCount 50 3 3 5 3 upper 0.01 2020

Once the server is running queries can be performed as following:

curl localhost:2020?sentence=Hefeweizenbier

and will return the decompounded word using whitespaces:

Hefe weizen bier

Evaluation

For the evaluation the python script eval_decompounding.py can be used. It expects as stdin a tab separated file including the gold standard and the predicted splits which are separated with dashs (-).

Here an example for a compound file (compound_file) where column 1 contains the predicted splits and column 2 the gold standard split

cat compound_file | python eval_decompounding.py 1 2 

This command results to the following output (precision, recall and F1 measure based on the splits (the third row rounds to 4 digits). The last column presents the number of compounds considered and the amount of entirely correctly split compounds as well as the rate of correct split compounds.

Precision       Recall       F1
0.986667        0.964335     0.975373
0.9867 &        0.9643&      0.9754
Considered      Correct      Percentage of Correct ones
700.000000      662.000000   0.945714

A detailed output of correct and false splits can be enabled by providing a third parameter:

cat compound_file | python eval_decompounding.py 1 2 debug

Significance testing

In addition to the above evaluation script, we also provide an evaluation script that can be used to compute a significance test between two methods. For this we use a Wilcoxon rank sum test and compare the performance based on the F1 score of each split compound. The test can be started using the following command:

python eval_decompounding_wilcoxon.py compound_file_1 predicted_compound gold_compound compound_file_2 predicted_compound gold_compound

Precomputed Models

We provide precomputed models for German and Dutch, which can be downloaded here: data.zip

In addition, we have computed further models using Wikipedia for various languages. In order to compute similarities, we have either used JoBimText or Word2Vec. The different models are here for the following languages:

Datasets for Evaluation

In order to perform an evaluation based on several languages in an automatic fashion, we have extracted compound words from Wiktionary for various langauges. These are available here: datasets

Citation

If you use SECOS, please cite the following work:

@inproceedings{riedl2016secos,
    title={Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods },
    author={Martin Riedl, Chris Biemann},
    booktitle={Proceedings of The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologie},
    pages={617--622},
    year={2016},
    address={San Diego, CA, USA},
}

License

Apache License (ASL) 2.0