User:Gang Chen/GSoC 2013 Progress

GSOC 2013

I'm working with Apertium for the GSoC 2013, on the project "Sliding Window Part of Speech Tagger for Apertium".

my proposal is here: Proposal

SVN repo

1. the tagger https://svn.code.sf.net/p/apertium/svn/branches/apertium-swpost/apertium

2. en-es language pair(for experiment) https://svn.code.sf.net/p/apertium/svn/branches/apertium-swpost/apertium-en-es

3. es-ca language pair(for experiment) https://svn.code.sf.net/p/apertium/svn/branches/apertium-swpost/apertium-es-ca

Usage

Command line

   HMM tagger usage:
   apertium-tagger -t 8 es.dic  es.crp  apertium-en-es.es.tsx  es-en.prob
   apertium-tagger -g es-en.prob.new

   SW tagger usage:
   apertium-tagger -w -t 8 es.dic  es.crp  apertium-en-es.es.tsx  es-en.prob
   apertium-tagger -w -g es-en.prob.new

Tagging Experiments

1. evaluation scripts

In the apertium-swpost/apertium-xx-yy (e.g. apertium-en-es) package, we have 2 tiny scripts:

apertium-en-es/es-tagger-data/extract_word_pos.py

apertium-en-es/es-tagger-data/eval.py

2. run the whole training and evaluation pipeline

To execute a whole training and evaluation prodecure, please refer to thesse 2 scripts:

apertium-en-es/step1_preporcess.sh

apertium-en-es/step2_train_tag_eval.sh

3. Spanish exp

language = es

https://svn.code.sf.net/p/apertium/svn/branches/apertium-swpost/apertium-en-es

test-set = The hand-tagged 1000+ lines in the en-es package.

training = Europal Spanish.

3.1. F1 scores

Euroal Lines	HMM(order 1)	LSW(-1, +1)	LSW(-1, +1, +2)	LSW(+1)	LSW(+1, +2)	LSW(-2, -1, +1)	LSW(-2, -1)	LSW(-1)
1	0.8194	0.8508	0.848465443901	0.849608928326	0.848465443901	0.8507	0.848556922655	0.850615194621
10	0.8311	0.8576	0.850066322097	0.85171293967	0.848419704524	0.8507	0.839454786626	0.82522984037
30	0.8605	0.8596	0.851438503408	0.854960435439	0.849060055802	0.8509	0.833097013219	0.815395874308
100	0.8703	0.8615	0.854182866029	0.857293143667	0.851575721539	0.8517	0.830261171843	0.813383341719
300	0.8778	0.8652	0.856286877373	0.857842016192	0.852719205964	0.8512	0.82445227096	0.818231715684
1000	0.8841	0.8677	0.859900288158	0.854777477931	0.854960435439	0.8516	0.823720440928	0.809998627819
3000	0.8875	0.8685	0.861638384485	0.854685999177	0.854548781046	0.8525	0.81937520011	0.808397749623
10000	0.8883	0.8688	0.864245528976	0.854823217308	0.855280611078	0.8516	0.819969812011	0.808306270869
30000	0.8883	0.8686	0.864565704615	0.854960435439	0.855280611078	0.8524	0.82152495083	0.808763664639
100000	0.8884	0.8687	0.865754928418	0.855372089832	0.855189132324	0.8543	0.821204775191	0.80890088277

Here is the graph:

3. Catalan exp

language = ca

https://svn.code.sf.net/p/apertium/svn/branches/apertium-swpost/apertium-es-ca

test-set = The hand-tagged 1000+ lines in the es-ca package.

training = Catalan Wikipedia.

3.1. F1 scores

cawiki lines	HMM	LSW(-1, +1)	LSW(-1, +1, +2)	LSW(+1, +2)	LSW(+1)	LSW(-2, -1, 1)	LSW(-2, -1)	LSW(-1)
1	0.82379762896	0.852186669839	0.852821061814	0.852821061814	0.852186669839	0.852186669839	0.852186669839	0.852186669839
10	0.830101899211	0.866539788272	0.856548114666	0.859680425043	0.861662899964	0.853574402284	0.823757979462	0.794139804131
30	0.848419967487	0.870108243131	0.85872883708	0.86201974545	0.863486776892	0.852583164823	0.803695333254	0.772848023473
100	0.865627849808	0.873993893977	0.865627849808	0.865429602316	0.866777685262	0.853693350779	0.78708219341	0.770350105071
300	0.875778121407	0.876333214385	0.869711748146	0.865271004322	0.867412077237	0.852265968835	0.780540026169	0.768565877642
1000	0.880417112724	0.881685896673	0.87569882241	0.869077356171	0.869553150153	0.852226319337	0.775623488363	0.768565877642
3000	0.888703857896	0.884738908053	0.881447999683	0.872447563538	0.869434201657	0.85190912335	0.773442765949	0.768684826137
10000	0.889258950874	0.886364537489	0.883470124103	0.871218429087	0.867887871218	0.850679988898	0.772887672971	0.767614289679
30000	0.889734744855	0.887316125451	0.886126640498	0.871377027081	0.868561912692	0.85139367987	0.771420641529	0.767733238175
100000	0.890963879307	0.886681733476	0.887038578962	0.870425439118	0.868720510686	0.851552277864	0.771301693034	0.7670988462

Here is the graph:

General Progress

2013-07-26: Map tags between TnT tagger and LSW tagger.

2013-07-17: Refine LSW tagger code for efficiency.

2013-07-06: Reconstruct tagger data using inheritance. Delete the intermedia SW tagger implementation.

2013-06-28: LSW tagger working, with rules.

2013-06-20: LSW tagger working, without rules.

2013-06-11: SW tagger working. Evaluation scripts working.

2013-05-30: Start.

Week Plan and Progress

week	date	plans	progress
Week -2	06.01-06.08	Community Bonding	(1) SW tagger (1st version) working. (2) Evaluation scripts (1st version) working for Recall precision and F1-score.
Week -1	06.09-06.16	Community Bonding	(1) LSW tagger (1st version) working, without rules.
Week 01	06.17-06.23	Implement the unsupervised version of the algorithm.	(1) LSW tagger working, without rules. (2) Check for and fix bugs.
Week 02	06.24-06.30	Implement the unsupervised version of the algorithm.	(1) LSW tagger working, with rules. (2) Update evaluation scripts for unknown words.
Week 03	07.01-07.07	Store the probability data in a clever way, allowing reading and edition using linguistic knowledge. Test using linguistic edition.	(1) Reconstruct tagger-data using inheritance. (2) Experiment on "How the iteration number affects the tagger performance?"(Conclusion: usually less than 8.) (3) Extract large corpus from Wikipedia for Spanish and Catalan for text amount experiments.
Week 04	07.08-07.14	Make tests, check for bugs, and documentation.	(1) Refine LSW tagger code for efficiency. (2) Experiment on "How the text amount affects the tagger performance?" (Conclusion: usually 1000+ lines will be OK) (3) Make stability tests.
Deliverable #1		A SWPoST that works with probabilities.
Week 05	07.15-07.21	Implement FORBID restrictions, using LSW. Using the same options and TSX file as the HMM tagger.	(1) Refine LSW tagger code for efficiency. (2) Implement LSW tagger with different window sizes for Catalan and Spanish and experiment them with different amounts of text. (Conclusion: window -1,+1 works best of all).
Week 06	07.22-07.28	Implement FORBID restrictions, using LSW. Using the same options and TSX file as the HMM tagger.	(1) Study the TnT tagger for preparing English tagged text. (2) Map tags between TnT tagger and LSW tagger. 6000 ambigous words were automatically mapped out of the total 8000.
Week 07	07.29-08.04	Implement ENFORCE restrictions, using LSW. Using the same options and TSX file as the HMM tagger.	(1) Hand-tag the 2000 words that were not mapped automatically.
Week 08	08.05-08.11	Make tests, check for bugs, and documentation.
Deliverable #2		A SWPoST that works with FORBID and ENFORCE restrictions.
Week 09	08.12-08.18	Implement the minimized FST version.
Week 10	08.19-08.25	Refine code. Optionally, implement the supervised version of the algorithm.
Week 11	08.26-09.01	Make tests, check the code and documentation. Optionally, Study further possible improvements.
Week 12	09.02-09.08	Make tests, check the code and documentation.
Deliverable #3		A full implementation of the SWPoST in Apertium.

Detailed progress

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2013-07-26

1. Study the usage of the TnT tagger.

2. Study the relationship between tagset of the TnT tagger and that of the Apertium tagger.

3. Develop algorithms to do automatic mapping between the two tagsets, 80% of the ambiguous words are successfully mapped.

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2013-07-15

1. Follow the training and tagging procedure step by step, using a small corpus of several sentences, so that parameters can be calculated by hand.

2. No significant bugs found during the training and tagging procedure.

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2013-07-10

1. Managed to get the HMM-supervised running.

2. Do experiments with window +1+2, -1+1+2. The results show that the RIGHT contexts are more important than the left contexts.

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2013-07-06

1. Reconstruct 'tagger_data' class, using INHERITANCE for HMM and LSW respectively.

2. Make relevant changes to the surroundings, including tagger, tsx_reader, hmm, lswpost, filter-ambiguity-class, apply-new-rules, read-words, etc.

3. DELETE the swpost tagger, which serves as an intermedia implementaion, replaced by the final lsw tagger, which works fine and could support rules.

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2013-07-03

1. update evaluation scripts, putting the unknown words into consideration. Now the evaluation script can report Recall, Precision, and F1-Score.

2. experiment with different amounts of text for training the LSW tagger.

3. experiment with different window sizes for training the LSW tagger.

4. experiments show strange results.

5. checking implementation, in case of potential bugs.

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2013-06-28

1. add find_similar_ambiguity_class for the tagging procedure. So a new ambiguity class won't crash the tagger down.

2. bugfix to the normalize factor. This makes the things right, but no improvements are gained to the quality.

3. replace the SW tagger with the LSW tagger. So the "-w" option is owned by the LSW tagger.

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2013-06-23

1. implement a light sliding-window tagger. This tagger is based on the SW tagger, with "parameter reduction" described in the 2005 paper.

2. add rule support for light-sw tagger. The rules help to improve the tagging quality.

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2013-06-21

1. add ZERO define. Because there are some double comparisons, we need a relatively precise threshold.

2. bug fix for the initial procedure and iteration formula.

3. check function style so the new code is consistent to the existing code.

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2013-06-20

1. use heap space for 3-dimensional parameters. This makes it possible to train successfully without manually setting the 'stack' environment.

2. add retrain() function for SW tagger. The logic of the SW tagger's retrain is the same as that of the HMM tagger. It append several iterations based on the current parameters.

3. bugfix, avoid '-nan' parameters.

4. the tagger_data write only non-ZERO values. This saves a lot of disk space, reducing the parameter file from 100M to 200k.

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2013-06-19

1. add print_para_matrix() for debugging in SW tagger. This funciton only prints non-ZERO parameters in the 3d matrix.

2. add support for debug, EOS, and null_flush. This makes the tagger work stable when called by other programs.

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2013-06-17

1. a deep follow into the morpho_stream class, and make sense its memembers and functions.

2. refine the reading procedure of the SW tagger, so that the procedure is simpler and more stable.

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2013-06-13

1. fix a bug in tagging procedure, where the initial tag score should be -1 instead of 0.

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2013-06-12

1. add option "-w" for sw tagger. The option "-w" is not a drop-in replacement to the current HMM tagger, but an extension. So the default tagger for Apertium will still be the HMM tagger. If the "-w" option is specified, the the SW tagger will be used.

2. add support for judging the end of morpho_stream.

3. The first working version is OK:)

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2013-06-11

1. Fix the bug of last version, mainly because of the read() method in 'TSX reader'. The read and write methods in the tsx_reader and tagger_data are re-implemented, because they are different from those for the HMM.

2. Implement the compression part of the SW tagger probabilities. These parameters are stored in a 3-d array.

3. Doing some debugging on the HMM tagger, in order to see how a tagger should work togethor with the whole pipeline.

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2013-06-10

1. Implement a basic version SW tagger. But there are bugs between them.

2. The training and tagging procedures strictly follow the 2004 paper.

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2013-05-30

1. Start the project.