Talk:Ideas for Google Summer of Code

So, was your organization a part of the google summer of code last year too?

Nope, but we're hoping to be included this year -- Francis Tyers 02:45, 16 March 2008 (UTC)

From old Projects page

Writing extensions to Apertium could be the ideal undergraduate (major) project. Here are some suggestions, along with brief outlines for how you might go about starting it.

A word compounder for Germanic languages

Most Germanic languages have compound words, we can analyse the compounds using LRLM (see Agglutination and compounds), but we cannot generate them without having them in the dictionary (a laborious task). The idea of this project it to create a post-generation module that takes series of words, e.g. in Afrikaans:

 vlote bestorming fase
 naval assault    phase

and turn them into compounds:

 vlootbestormingfase
 naval+assault+phase

We don't want to compound all words, but it might be a good idea to compound those which have been seen before . There are many large wordlists of compound words that could be used for this. Of course if they aren't found maybe some kind of heuristics could be used. Probably we'd only want to compound where words are >= 5 characters long.

Automatic accent and diacritic insertion

One of the problems in machine translating text in real time chat environments (and generally) is the lack of accents or diacritic marks. This makes machine translation hard, because without the (´), traducción is an unknown word.

There is a need for a module for Apertium which would automatically replace the accents/diacritics on unaccented/diacritic'd words.

References

• Simard, Michel (1998). "Automatic Insertion of Accents in French Texts". Proceedings of EMNLP-3. Granada, Spain.
• Rada F. Mihalcea. (2002). "Diacritics Restoration: Learning from Letters versus Learning from Words". Lecture Notes in Computer Science 2276/2002 pp. 96--113

Old ideas

Task	Difficulty	Description	Rationale	Requirements	Interested mentors
Porting read more...	4. Entry level	Port Apertium to Windows complete with nice installers and all that jazz. Apertium currently compiles on Windows (see Apertium on Windows)	While we all might use GNU/Linux, there are a lot of people out there who don't, some of them use Microsoft's Windows. It would be nice for these people to be able use Apertium too.	C++, autotools, experience in programming on Windows.
Tree-based transfer read more...	1. Very hard	Create a new XML-based transfer language for tree-based transfer and a prototype implementation, and transfer rules for an existing language pair.	Apertium currently works on finite-state chunking, which works well, but is problematic for less-closely related languages and for getting the final few percent in closely-related languages. A tree-based transfer would allow us to work on real syntactic constituents, and probably simplify many existing pairs. There are some existing non-free implementations.	XML, Knowledge of parsing, implementation language largely free.
Interfaces	4. Entry level	Create plugins or extensions for popular free software applications to include support for translation using Apertium. We'd expect at least Firefox and Evolution (or Thunderbird), but to start with something more easy we have half-finished plugins for Pidgin and XChat that could use some love. The more the better! Further ideas on plugins page	Apertium currently runs as a stand alone translator. It would be great if it was integrated in other free software applications. For example so instead of copy/pasting text out of your email, you could just click a button and have it translated in place. This should use a local installation with optional fallback to the webservice.	Depends on the application chosen, but probably Java, C, C++, Python or Perl.
Automated lexical extraction	2. Hard	Writing a C++ wrapper around Markus Forsberg's Extract tool (version 2.0) as a library to allow it to be used with Apertium paradigms and TSX files / Constraint grammars as input into its paradigms and constraints.	One of the things that takes a lot of time when creating a new language pair is constructing the monodices. The extract tool can greatly reduce the time this takes by matching lemmas to paradigms based on distribution in a corpus.	Haskell, C++, XML
Bytecode for transfer	2. Hard	Adapt transfer to use bytecode instead of tree walking.	Apertium is pretty fast, but it could be faster, and the transfer is dominating the CPU usage. This task would be write a compiler and interpreter for Apertium transfer rules into the format of an an off-the-shelf bytecode engine (e.g. Java, v8, kjs, ...). If Java bytecode was chosen this might eventually make Apertium run on J2ME devices. See also: Bytecode for transfer	C++ and for the bytecode Java or Javascript
VM for the transfer module read more...	3. Medium	VM for the current transfer architecture of Apertium and for the future transfers, pure C++	Define an instruction set for a virtual machine that processes transfer code, then implement a prototype in Python, then porting to C++. The rationale behind this is that XML tree-walking is quite slow and CPU intensive. In modern (3 or more stage) pairs, transfer takes up most of the CPU. There are other options, like Bytecode for transfer, but we would like something that does not require external libraries and is adapted specifically for Apertium.	Python, C/C++, XML, XSLT, code optimisation, JIT techniques, etc.	Sortiz
Linguistically-driven bilingual-phrase filtering for inferring transfer rules	3. Medium	Re-working apertium-transfer-training-tools to filter the set of bilingual phrases automatically obtained from a word-aligned sentence pair by using linguistic criteria.	Apertium-transfer-training-tools is a cool piece of software that generates shallow-transfer rules from aligned parallel corpora. It could greatly speed up the creation of new language pairs by generating rules that would otherwise have to be written by human linguists	C++, general knowledge of GIZA++, Perl considered a plus.	Jimregan
Context-dependent lexicalised categories for inferring transfer rules	2. Hard	Re-working apertium-transfer-training-tools to use context-dependent lexicalised categories in the inference of shallow-transfer rules.	Apertium-transfer-training-tools generates shallow-transfer rules from aligned parallel corpora. It uses an small set of lexicalised categories, categories that are usually involved in lexical changes, such as prepositions, pronouns or auxiliary verbs. Lexicalised categories differentiate from the rest of categories because their lemmas are taken into account in the generation of rules.	C++, general knowledge of GIZA++, XML.	Jimregan
Corpus-assisted dictionary expansion	4. Entry level	Semi-automatic bilingual word equivalence retrieval from a bitext and a monolingual word list.	Improve an existing Python script to retrieve the best translations (suggestions) of a word (typically an unknown word) given a particular parallel text corpus. Perhaps combine the result with automatic paradigm guessing (also suggestions) to improve the productivity of the lexical work for most contributors	Python, C/C++, AWK, Bash, perhaps web interface in PHP, Python, Ruby on Rails	Sortiz, Jimregan
Detect 'hidden' unknown words read more...	3. Medium	The part-of-speech tagger of Apertium can be modified to work out the likelihood of each 'tag' in a certain context, this can be used to detect missing entries in the dictionary.	Apertium dictionaries may have incomplete entries, that is, surface forms (lexical units as they appear in running texts) for which the dictionary does not provide all the possible lexical forms (consisting of lemma, part-of-speech and morphological inflection information). As those surface form for which there is at least one lexical form cannot be considered unknown words, it is difficult to know whether all lexical forms for a given surface form have been included in the dictionaries or not. This feature will detect 'possible' missing lexical forms for those surface forms in the dictionaries.	C++ if you plan to modify the part-of-speech tagger; whatever if rewriting it from scratch.	Felipe Sánchez-Martínez
Improvements to target-language tagger training read more...	2. Hard	Modify apertium-tagger-training-tools so that it can deals with n-stage transfer rules when segmenting the input source-language text, and applies a k-best viterbi pruning approach that does not require to compute the a-priori likelihood of every disambiguation path before pruning.	apertium-tagger-training-tools is a program for doing target-language tagger training, meaning it improves POS tagging performance specifically for the translation task, achieving a result for unsupervised training comparable with supervised training. This task would also require switching the default perl-based language model to either IRSTLM or RandLM (or both!).	C++, XML, XSLT	Felipe Sánchez-Martínez
Hybrid MT	2. Hard	Building Apertium-Marclator rule-based/corpus-based hybrids	Both the rule-based machine translation system Apertium and the corpus-based machine translation system Marclator do some kind of chunking of the input as well as use a relatively straightforward left-to-right machine translation strategy. This has been explored before but there are other ways to organize hybridization which should be explored (the mentor is happy to discuss). Hybridization may make it easier to adapt Apertium to a particular corpus by using chunk pairs derived from it.	Knowledge of Java, C++, and scripting languages, and appreciation for research-like coding projects	Mlforcada, Jimregan

Old further reading

Automated lexical extraction

• M. Forsberg H. Hammarström A. Ranta. "Morphological Lexicon Extraction from Raw Text Data". FinTAL 2006, LNAI 4139, pp. 488--499.

Support for agglutinative languages

• Beesley, K. R and Karttunen, L. (2000) "Finite-State Non-Concatenative Morphotactics". SIGPHON-2000, Proceedings of the Fifth Workshop of the ACLSpecial Interest Group in Computational Phonology, pp. 1--12,

Koichi Takeda "Pattern-Based Context-Free Grammars for Machine Translation"

Gábor PRÓSZÉKY and László TIHANYI "MetaMorpho: A Pattern-Based Machine Translation System"