1. Language technology in Africa: Prospects Arvi Hurskainen University of Helsinki

2. Why LT for African languages? LT is currently considered a necessary field of development in most languages. Why should African languages be neglected?

3. Current state Compared with other continents, LT in Africa takes its first steps.

4. Current state The latest issue of MultiLingual, a periodical with 15,000 subscribers, was supposed to concentrate on LT in Africa. The only article discussing genuine LT was the one describing Swahili Language Manager (SALAMA) Another article on Africa was written by a freelancer on public domain localization in South Africa. That was all for Africa.

5. In LT the gap between well- resourced and poorly resourced languages is bigger than in any other field. My impression is that even today half of global investments on LT goes to English.

6. African languages are triply handicapped: –Commercial sector not interested –Local governments poor – little or no public support –African languages have features that need different approaches than those used in main-stream LT

7. Language technology (LT) Labour-intensive –Trivial results quickly –Useful results require several man-years of work Although the development of LT is expensive, the results can be very rewarding

8. Language technology (LT) LT built on a modular basis can result in several kinds of applications –An additional application can make use of earlier modules and thus costs can be reduced Once developed, LT applications can be widely distributed with minimal cost

9. Language technology (LT) Experience of LT in other languages available –Wrong tracks can be avoided –Solutions applied in other languages can be tested in African languages

10. Language technology (LT) LT of African languages NOT mere application from other languages African languages have special features –Very rich morphology –Noun classes –Complex verb formation –Serial verbs –Non-concatenative processes –Reduplication –Inflecting idioms and other multi-word expressions –Tones Lexical Grammatical

11. Feasibility of LT in Africa Question: If African languages have several special features regarded as ’problems’, is it feasible to develop language technology for those languages? Answer: Some ‘problems’ can be turned into advantages

12. Rich morphology Requires efficient development environment to succeed, but Can be very useful in disambiguation (= choice of correct interpretation) and syntactic analysis.

13. Poor morphology vs. rich morphology Poor morphology (e.g. English) –Easy to analyze morphologically, but –Difficult to disambiguate and analyze syntactically and semantically Rich morphology (e.g. Bantu languages) –Difficult to analyze morphologically, but –Less difficult to disambiguate and analyze syntactically

14. LT applications Applications for end-users: –spelling correctors –hyphenators –grammar checkers –thesauri –electronic dictionaries –MT applications –multilingual speech applications

15. LT applications Applications for developers: –dictionary compilers –dictionary evaluators –MT development environments –information retrieval and data mining

16. Machine Translation (MT) Text-to-text MT –Official texts (government, AU, UN, SADDEC, business, manuals, teaching) –News texts –Communication through email in international organizations Speech-to-speech MT –Simultaneous interpretation –Multilingual phone calls

17. Phases of speech-to-speech MT 1.Speech recognition Transforming speech signal to text 2.Tokenization of text Identifying ‘words’, punctuation marks, diacritics etc. 3.Morphological analysis Analyzing each morphological unit and providing it with codes (tags) 4.Morphological disambiguation Determining correct interpretation

18. Phases of speech-to-speech MT 5. Syntactic mapping Providing words with syntactic tags 6.Semantic disambiguation Choosing the correct semantic meaning 7.Multi-word units Isolating multi-word expressions and giving correct interpretation Idioms Proverbs Adjectival expressions Compound nouns Serial verb constructions

19. Phases of speech-to-speech MT 8.Managing word order Re-ordering word sequences to meet the rules of the target language Inclusion and exclusion of pronouns and articles 9.Producing surface forms of target language 10.Clean text in target language 11.Text-to-speech conversion

20. 1. Tokenization *mtu aliyepata taarifa alipiga simu, kukaa na kungoja

21. 2. Morphological analysis *mtu "mtu" N CAP 1/2-SG { the } { man } aliyepata "pata" V 1/2-SG3-SP VFIN { he/she } PAST 1/2-SG-REL { who } z [pata] { get } SVO taarifa "taarifa" N 9/10-SG { the } { report } AR "taarifa" N 9/10-PL { the } { report } AR alipiga "piga" V 1/2-SG3-SP VFIN { he/she } PAST z [piga] { hit } SVO ACT "piga" V 1/2-SG3-SP VFIN { he/she } PR:a 5/6-SG-OBJ OBJ { it } z [piga] { hit } SVO ACT simu "simu" N 9/10-SG { the } { telephone } "simu" N 9/10-SG { the } { type of sardine or sprat } AN "simu" N 9/10-PL { the } { telephone } "simu" N 9/10-PL { the } { type of sardine or sprat } AN, "," COMMA {, } kukaa "kaa" V INF { to } z [kaa] { sit } SV SVO "kaa" V INF NO-TO z [kaa] { sit } SV SVO na "na" CC { and } "na" AG-PART { by } "na" PREP { with } "na" NA-POSS { of } "na" ADV NOART { past } kungoja "ngoja" V INF { to } z [ngoja] { wait } SV "ngoja" V INF NO-TO z [ngoja] { wait } SV

22. 3. Disambiguation, isolating MWE *mtu "mtu" N 1/2-SG { the } { man } @SUBJ aliyepata "pata" V 1/2-SG3-SP VFIN { he/she } PAST 1/2-SG-REL { who } z [pata] { get } SVO @FMAINVtr+OBJ> taarifa "taarifa" N 9/10-SG { the } { report } AR @OBJ alipiga "piga" V 1/2-SG3-SP VFIN { he/she } PAST z SVO ACT IDIOM-V> @FMAINVtr-OBJ> simu "simu" <IDIOM { call }, "," COMMA {, } kukaa "kaa" V INF { to } z [kaa] { sit } SV SVO @-FMAINV-n "kaa" V INF NO-TO z [kaa] { sit } SV SVO @-FMAINV-n na "na" CC { and } @CC kungoja "ngoja" V INF { to } z [ngoja] { wait } SV SVO @-FMAINV-n "ngoja" V INF NO-TO z [ngoja] { wait } SV SVO @-FMAINV-n

23. 4. Isolating MWE ( N 1/2-SG { the } { man } @SUBJ ) ( V 1/2-SG3- SP VFIN { he/she } PAST 1/2-SG-REL { who } z { get } SVO @FMAINVtr+OBJ> ) ( N 9/10-SG { the } { report } @OBJ ) ( V 1/2-SG3-SP VFIN { he/she } PAST z SVO ACT IDIOM-V> @FMAINVtr-OBJ> <IDIOM { call } ) ( COMMA {, } ) ( V INF { to } z { sit } SV SVO @-FMAINV-n ) ( CC { and } @CC ) ( V INF { to } z { wait } SV @-FMAINV-n )

24. 5. Word-per-line format ( N 1/2-SG { the } { man } @SUBJ ) ( V 1/2-SG3-SP VFIN { he/she } PAST 1/2-SG-REL { who } z { get } SVO @FMAINVtr+OBJ> ) ( N 9/10-SG { the } { report } @OBJ ) ( V 1/2-SG3-SP VFIN { he/she } PAST z SVO ACT IDIOM-V> @FMAINVtr-OBJ> <IDIOM { call } ) ( COMMA {, } ) ( V INF { to } z { sit } SV SVO @-FMAINV-n ) ( CC { and } @CC ) ( V INF { to } z { wait } SV @-FMAINV-n )

25. 6. Copying info on serial verbs ( N 1/2-SG { the } { man } @SUBJ ) ( V 1/2-SG3-SP VFIN PAST 1/2-SG-REL { who } z { get } SVO @FMAINVtr+OBJ> ) ( N 9/10-SG { the } { report } @OBJ ) ( V 1/2-SG3-SP VFIN PAST z SVO ACT IDIOM-V> @FMAINVtr-OBJ> <IDIOM { call } ) ( COMMA {, } ) ( V 1/2-SG3-SP VFIN PAST z { sit } SV SVO @FMAINV-n ) ( CC { and } @CC ) ( V 1/2-SG3-SP VFIN PAST z { wait } SV SVO @FMAINV-n )

26. 7. Construct word order ( N 1/2-SG { the } { man } @SUBJ ) ( V 1/2-SG3-SP VFIN PAST 1/2-SG-REL { who } z { get } SVO @FMAINVtr+OBJ> ) ( N 9/10-SG { the } { report } @OBJ ) ( V 1/2-SG3-SP VFIN PAST z SVO ACT IDIOM-V> @FMAINVtr-OBJ> <IDIOM { call } ) ( COMMA {, } ) ( V 1/2-SG3-SP VFIN PAST z { sit } SV SVO @FMAINV-n ) ( CC { and } @CC ) ( V 1/2-SG3-SP VFIN PAST z { wait } SV SVO @FMAINV-n )

27. 8. Surface form in target language ( N 1/2-SG { the } { man } @SUBJ ) ( V 1/2-SG3-SP VFIN PAST 1/2-SG-REL { who } z { :got } SVO @FMAINVtr+OBJ> ) ( N 9/10-SG { the } { report } @OBJ ) ( V 1/2-SG3-SP VFIN PAST z SVO ACT IDIOM-V> @FMAINVtr-OBJ> <IDIOM { :called } ) ( COMMA {, } ) ( V 1/2-SG3-SP VFIN PAST z { :sat } SV SVO @-FMAINV-n ) ( CC { and } @CC ) ( V 1/2-SG3-SP VFIN PAST z { :waited } SV @-FMAINV-n ) Translation: the man who got the report called, sat and waited

28. Organizing the work How should the work be organised on the continent of hundreds of languages? Prioritising languages –‘Big’ languages first due to their strategic importance –Some minor languages may have special political or scientific importance

29. Organizing the work Scientific infrastructure –Such as ELRA (European Language Resource Association) and –ELDA (European Language Resource Distribution Agency) Africa needs something similar An initiative was made in the LREC2006 conference in Genova to establish such an infrastructure

30. Organizing the work Networking extremely important –Geographical distances between actors are immense –Ensures efficient communication and distribution of ideas –Ensures that the best and tested approaches will become a standard in LT –Motivates in this tough work

31. Networking A Wikipedia type forum as an information and discussion centre for LT in Africa http://forums.csc.fi/kitwiki/pilot/view/ KitWiki/Community/AfricanActiviti es

32. KitWiki/Community/AfricanActivities Organizations, networks and activities related to LT for African languages Key Areas LT Policy LT Resources Helsinki Corpus of Swahili Corpus Of SwahiliCorpus Of Swahili LT Research and Development SALAMA - Swahili Language Manager SALAMASALAMA Nordic Journal of African Studies NJASNJAS LT Training and Education LT Legislation LT Business Activities Other Activities This topic: KitWiki/Community > WebHome > AfricanActivitiesWebHome AfricanActivities History: r3 - 29 Jul 2006 - 09:03 - ArviHurskainenArviHurskainen

33. EDULINK initiative EU has started in 2006 to support networking between higher education institutions EDULINK-ACP-EU Cooperation Programme in Higher Education

34. EDULINK initiative EDULINK is the first ACP-EU Cooperation Programme in Higher Education EDULINK is financed by the European Commission under the 9th EDF and is managed by the ACP Secretariat.

35. EDULINK initiative EDULINK promotes networking of HEIs in ACP States and the eligible EU Member States through funding of joint projects.

36. EDULINK initiative: Language technology for African languages Consortium of five universities –Dar-es-Salaam –Nairobi –Ghana –Hawassa (Ethiopia) –Helsinki Associates –UNISA, Stellenbosch, SA –Trondheim

37. EDULINK initiative: Language technology for African languages Aims –Training in LT Workshops Training courses Summer School in LT Evaluation –Developing new LT Language corpora Morphological parsers Speech technology MT (further development of SALAMA)

38. Development environments Environments with property rights –Can be obtained through licensing for development purposes –Can also be available with nominal price, e.g. xfst package of Xerox –Cannot be included into the product without a separate agreement with the property owner

39. Development environments Open domain environments –Free for development –Free for inclusion into a product

40. Availability of development environments In morphology –xfst package of Xerox using finite state methods is most popular –Free for development but not free for inclusion into a product

41. Availability of development environments In disambiguation and syntactic mapping –CG-2 and Functional Dependency Grammar (FDG) of Connexor Only through licensing Not free for inclusion into a product

42. Availability of development environments In disambiguation and syntactic mapping –CG-3 is an open source product Free for developing Free for inclusion into a product http://beta.visl.sdu.dk/constraint_gr ammar.html

43. Developing open source technology Efforts to move SALAMA to open domain

44. Two implementations of SALAMA Comparison of two methods for morphological analysis –Analysis using finite state method (PR) and –Analysis using two-phase method (OS)

45. Two implementations of SALAMA Finite state method –Good Very fast, 4.500 w/s in SWATWOL Facilitates description on more than one level –Two-level description most common

46. Two implementations of SALAMA Finite state method –Good The use of two-level rules simplifies the structure of the dictionary The whole morphology can be described in one phase Can be used for simulating linguistic processes (good for research purposes)

47. Two implementations of SALAMA Finite state method –Bad Difficult in handling non- concatenative processes (does not ‘see behind’) Writing a reliable rule system is difficult In constructing the lexicon, the influence of the rules must be anticipated

48. Two implementations of SALAMA Finite state method –Bad Because the lexicon is a tree- structure, the whole language should be described with one single lexicon Difficulties in compiling very large lexicons No open source platform available

49. Two implementations of SALAMA Two-phase method - description –In the first phase, the word is described using pattern matching rules Produces meta-tags with two parts Example: –unanifundisha “fundisha” [funda] V uSP naTAM niOBJ ishaVE –uSP »u = string in the word »SP = tag meaning subject prefix

50. Two implementations of SALAMA Two-phase method –In the second phase, meta-tags are rewritten as final tags –uSP > –1/2-SG2-SP VFIN { you } –3/4-SG-SP VFIN { it } –11-SG-SP VFIN { it }

51. Two implementations of SALAMA Result after the first phase: unanifundisha “fundisha” [funda] V uSP naTAM niOBJ ishaVE Result after the second phase: unanifundisha "fundisha" [funda] V 1/2-SG2-SP VFIN { you } PR:na 1/2-SG1-OBJ { me } { teach } CAUS "fundisha" [funda] V 3/4-SG-SP VFIN { it } PR:na 1/2-SG1-OBJ { me } { teach } CAUS "fundisha" [funda] V 11-SG-SP VFIN { it } PR:na 1/2-SG1-OBJ { me } { teach } CAUS

52. Two implementations of SALAMA Two-phase method –Good No specific development platform needed Task divided into two phases – makes the description of each phase more manageable No compilation problems, because the system is composed of a number of separate rules, each performing a specific task

53. Two implementations of SALAMA Two-phase method –Good Optimal order of readings can be controlled – helps in disambiguation, No ownership restrictions The product free for distribution

54. Two implementations of SALAMA Two-phase method –Bad Requires fairly good programming skills Because two-level rules cannot be used for simplifying the lexicon, the lexicon becomes complex The absence of state transition (found in fst methods) increases the need for copying word stems in complex word structures, e.g. verbs in Bantu languages

55. Two implementations of SALAMA The complexity of the lexicon can be reduced by allowing some overproduction, which will be removed afterwards with rules that check ungrammatical tag combinations

56. Two implementations of SALAMA Example: reciprocal and passive extensions block the object prefix –wananifundishana (ungrammatical) “fundishana” [funda] V waSP naTAM niOBJ ishanaVE In post-processing, the string will be removed with the rule that states that niOBJ and anaVE cannot co-occur

57. Two implementations of SALAMA Speed in morphological analysis –Finite state method: 4500 w/s –Two-phase method: 500 w/s Speed in machine translation –Finite state method: 650 w/s –Two-phase method: 350 w/s

58. Two implementations of SALAMA CG-3 in disambiguation and syntactic mapping is an open domain product

59. Two implementations of SALAMA Rules for re-ordering the sentence structure in the target language can be written with any suitable programming language

60. Two implementations of SALAMA Rules for producing the surface form in the target language can be written with any suitable programming language

61. Where? The main responsibility for developing LT for African languages should be in those countries where the languages are mostly used –Work out and implement a plan –Provide resources (human and capital) –Make use of the know-how available globally –Networking

62. Language resources Text corpora –Private collections of texts by researchers –Text and speech corpora of official languages of South Africa –Helsinki Corpus of Swahili (12 m) globally available Texts corrected and edited Morphologically annotated –Gikuyu annotated corpus (de Pauw et al)

63. Language resources Manuscripts –SOAS (School of Oriental and African Studies, London) holds a large collection of Swahili manuscripts –Background info in the Web, but not the manuscripts themselves

64. Language resources Corpus compilation in cooperation with publishing houses – so far very little used More extensive use of the material available in the Web

65. Dictionaries Internet Swahili Dictionary (Yale University) –Free and widely used (1 mil web visits monthly) –Compiled on voluntary basis NOTE! On Sep 5 2007 The Internet Living Swahili Dictionary has been taken offline – at least temporarily

66. Dictionaries TUKI (Taasisi ya Uchunguzi wa Kiswahili) has released a CD version of the –Swahili - English and –English – Swahili dictionaries –Can be edited and used in developing language tools

67. Grammars Electronic grammars missing SALAMA (Swahili Language Manager) contains a comprehensive grammar of Swahili SALAMA-DC has a potential of compiling extensive dictionaries with translated use examples

68. Tools Spell checkers based on word lists available for a number of languages –Kilinux (Kiswahili Linux) project Spell checkers based on linguistic analysis –Orthographix 2 for Swahili (Lingsoft) –Swahili speller integrated to MS Office 2007

69. Tools SALAMA - A comprehensive environment for developing various kinds of tools Spell checking Information retrieval Vocabulary compilation Concordance compilation Dictionary compilation Machine translation So far based on the language in text form

70. Projects in progress Localization to Swahili: –Windows 2000 and Windows XP (2006) –MS Office 2003 (2005) Open Swahili Localization Project (KILINUX) –Linux to Swahili –OpenOffice to Swahili, including a Swahili spell checker Ubuntu (a basic version of Linux) to Swahili and many other languages

71. Technology projects SALAMA –Based on linguistic knowledge –Maximal amount of linguistic information expressed overtly and systematically –Statistical probabilities used in semantic disambiguation –Developing environment rather than a task-specific tool –Modular structure –Extensible

72. Technology projects SALAMA –Current status: machine translation from Swahili to English Dictionary compilation –Future plans: machine translation from English to Swahili Integration to speech-to-speech applications

73. Open source platforms Need: open source platforms –Initiatives do exist for developing open source platforms for morphological analysis

74. Government support in Africa Detailed plans on how to proceed and how to finance the work still missing South Africa better organized than other areas Initiatives for networking –Networking the Development of Language Resources for African Languages (LREC 2006, Genova) –EDULINK Initiative (EU)

75. Summary Atmosphere positive Towards open source solutions Special features of African languages to be taken into account Systems rather than ad hoc solutions for individual problems Networking extremely important