This manual can be displayed by calling just WordAlign or alternatively WordAlign manual.

Word alignments in natural languages

This library and program are designed for the alignment of words in human languages.

Implemented with ideas described in:

1. Christian Hoener zu Siederdissen
   Sneaking Around ConcatMap: Efficient Combinators for Dynamic Programming
   2012, Proceedings of the 17th ACM SIGPLAN international conference on Functional programming
   paper preprint
2. Andrew Farmer, Christian Höner zu Siederdissen, and Andy Gill.
   The HERMIT in the stream: fusing stream fusion’s concatMap
   2014, Proceedings of the ACM SIGPLAN 2014 workshop on Partial evaluation and program manipulation.
   paper
3. Christian Höner zu Siederdissen, Ivo L. Hofacker, and Peter F. Stadler.
   Product Grammars for Alignment and Folding
   2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics. 99
   paper
4. Christian Höner zu Siederdissen, Sonja J. Prohaska, and Peter F. Stadler
   Algebraic Dynamic Programming over General Data Structures
   2015, BMC Bioinformatics
   preprint

Usage

Word alignments require three pieces of information: (i) the words to be aligned, (ii) the selection of an alignment algorithm, and (iii) a scoring scheme (either simple or bigram-based). Each of these is described below.

Words to be Aligned

Each word is encoded as a list of characters. A single character, however, is encoded as a list of unicode symbols, not just a single symbol.

This particular encoding is necessary because there is no general one-to-one mapping of atomic alphabet symbols to unicode characters. Just consider character decorations, such as Umlaute (even though Umlaute are available in unicode).

As user, you send a list to stdin that is formatted as follows (with all fields mandatory; the tab symbol being the separator between each column):

• word id
• language name
• meaning identifier (this is a string, not a number)
• length of the word
• space-separated characters (each character is actually a string)

(In case you read the plain-text version of this document, the 4 whitespace characters should be left out.)

0	Albanian_Tosk	1.100	5	\' b o t ə
2	Albanian_Tosk	1.100	10	r̃ o k u lʸ i a\' lʸ e m

Selection of Alignment Algorithm

The WordAlignment program comes with these modes:

Mode names can be shortened to unique prefixes.

Global Alignments

Global alignments use a NeedlemanWunsch style algorithm with linear gap costs.

Overhang alignments

Overhang alignments use affine gap scoring. Overhang alignments separate the alignment into three phases, the prefix, infix, and suffix phase. Typically, prefix and suffix phases have very low costs.

Options

Common default options

-? --help             Display help message
-V --version          Print version information
   --numeric-version  Print just the version number
-v --verbose          Loud verbosity
-q --quiet            Quiet verbosity

--verbose prints status information every 10.000 alignments

Common options for all alignments variants

   --simplescorefile=ITEM  the file to read the simple scores from
-l --lpblock=ITEM,ITEM     compare ONLY the given pair of languages: i.e
                           'Breton','Breton' or 2,3  (with the latter
                           notation '2' being the 2nd language in the input
                           file)
   --showmanual            show the manual and quit
   --filterscore=NUM       only print results with this score or higher
   --filterbacktrack=NUM   only provide backtracking results for results
                           with this score or higher

--simplescorefile expects a score file for simple unigram based scoring. An example file is provided under scores/defaultSimpleScoring. For bigram score files, use a file like scores/defaultBigramScoring.

--lpblock expects a pair of language names (Breton,Breton) or a pair of integers (3,3 or 4,6) and will then align only the given language pairs with each other. This option should be very helpful in case you want to parallelize the program.

--filterscore is used to limit printing results to only the alignments with score not lower than this option. Given that printing requires a significant amount of CPU time due to unicode conversion, this option improves performance substantially.

--filterbacktrack is used to limit printing of backtracking for a given alignment to the best results. Works like --filterscore but will always print the forward result.

--filternormalized applies filter on the length-normalized scores instead of the absolute ones.

Options for bigram alignment variants

-b --bigramscorefile=ITEM  the file to read the bigram scores from

--bigramscorefile is used to point toward a file with a list of bigram scores for all language pairs.

Simple score file description

Bigram score file description

In contrast to the simple model above, however, character matching is now performed in a bigram context.

The required score file is currently using an in-house format with the following columns all required (with whitespace, not tab between the entries):

• language name
• character (bigram 1.1)
• character (bigram 1.2)
• language name
• character (bigram 2.1)
• character (bigram 2.2)
• score

Three example lines

Albanian_Tosk \' a Albanian_Tosk \' a 4.25238
Albanian_Tosk \' a Albanian_Tosk \' b 0.402228
Albanian_Tosk \' a Albanian_Tosk \' g 1.07432

Example output

The output are four lines for each alignment. An info line with the word ids (IDS), the alignment score (SCORE), the normalized scores (NSCORE) and the actual words, started by (WORD) and interleaved by (WORD). The next two lines are the alignment, with deletions showning up as minus symbols - in the deletion field. Note that a deletion does not delete a character from the input, it merely aligns an existing character in one alignment with the symbol for deletion - in the other. The final line provides the per-column score for the alignment. After the alignment follows one empty line.

Words are written left-to-right in the information line, and bottom to top in the alignment.

IDS: 2 3 SCORE:  93.40 NSCORE:  10.38    WORD: ^ h a u s b a u $   WORD: ^ b a u m h a u s $
       b       a       u       m       h       a       u       s       $       -       -       -
       -       -       -       -       h       a       u       s       b       a       u       $
     0.0     0.0     0.0    -1.0    -3.0    33.9    33.8    33.7    -3.0    -1.0     0.0     0.0

Performance Notes

Measured on a core i5-3570K @ 3.40 GHz; single-threaded, based on 2001000 alignments.

The program is not compiled for multi-threading, if you need this consider the --lpblock option first and parallelize on the language pair level. Otherwise, send a mail.

When aligning short words, as occur in human language, the approximate number of alignments per second is:

and when printing alignments via --filterscore is restricted to scores >=10 to return about 0.6% of alignments:

Three- and Four-way Alignments

These are currently disabled. If you need them, consider contacting me.

Writing unit tests for WordAlign

First, prepare a version of WordAlign that includes the properties test program. This is done as follows with a sufficiently new version of cabal:

cabal new-configure --enable-tests
cabal new-build

Once done, take a look at the tests folder. The files with suffix .golden are named in a special manner:

1. grammar type global or infix
2. score system unigram or bigram
3. the score files used; for unigrams a single .ugdef file is required, for bigrams both a .bgdef and a .bgms file are required. .ugdef files hold a unigram scoring system. .bgdef files bigram default scores. Last, .bgms files hold scores for known bigrams.
4. the input words file with the words to be aligned to each other. From the file, all pairs are created where fst <= snd. I.e. the upper triangular matrix of words.
5. a number with the the co-optimals to backtrack. Normally, this should be 1 but a small number of tests should have a big number here -- say 99 and all possible co-optimals should be checked for correctness. This may be fewer than the given number but at least one backtrack should always be there.

Running the properties program will then automatically test each golden file vs the WordAlign algorithm together with the score files as inputs.

Automatic property are done with a simple ./properties and should be all green.

How to create new test files:

1. Create a new empty file named like this example here: global-bigram-PTSP800-PTpoSPpampa-1.golden.
2. This will test against the global Needleman-Wunsch algorithm with bigram scoring. The score file used is the PTSP800.bgms bigram score file and the PTSP800.bgdef file. Words are taken from the PTpoSPpampa.words file. Exactly one backtrack is generated.
3. Fill the necessary scoring and words files.
4. Run the properties program as follows: properties --accept -i. This should fill the previously empty golden file created in step 1.
5. Carefully check if the contents of this file are correct.
6. If so a run of ./properties should now only show green results. In particular, the file name created under 1. should show up as a new property that was tested.

Contact

Christian Hoener zu Siederdissen
Leipzig University, Leipzig, Germany
choener@bioinf.uni-leipzig.de
http://www.bioinf.uni-leipzig.de/~choener/