Document Type: Research paper


Economic Cost, Poor Sanitation, Safe Drinking Water


the pririty areas f Eurpean Unin plicy [4] and prcessing f the language and necessary fr the the natinal curses f the Eurpean cuntries, fr creatin and peratin f highly efficient the creatin f which attract the best specialists f technlgies f knwledge management, is an urgent public and cmmercial research institutins frm all need f mdem times. ver Eurpe thrugh prjects. he main tasks f these prjects are the plingMethds f the Cmputerizatin f the infrmatin sphere, n the f advances ne hand, stimulated the develpment f cuntries cmputatinal linguistics aimed at slving the perating prblems f the use f natural language mechanisms in using in autmated systems f varius types. n the ther innvative linguistic technlgies saves hand, the pssibility f using a cmputer in linguistic n firms research allws the analysis f large arrays f textual further nurtures develps the data t cnfirm r refute the theretical achievements the f mdern linguistics and t study the phenmena f natural language [2]. This is why cmputer Due t the multilingualism f the EU linguistics, such as autmatic natural language rganizatins and the penness f the brders frecgnitin [3], is at the frefrnt; creatin f Eurpean cuntries, special attentin was fcused n frmalized analyzers and synthesizers f natural prjects aimed at slving prblems f machine language; develpment f autmated infrmatin translatin and termingraphy. retrieval and data management systems; creatin f autmated ntlgies capable f supprting research Findings wrk, publishing specialist encyclpedic knwledge The histry f machine translatin systems was and bibligraphic infrmatin, helping t create started by IBM in 1946 in Gergetwn, USA. The multidimensinal classificatins f material; purpse f the prject was t create a Russian- machine translatin; generatin f electrnic crpra English machine translatin system fr the Pentagn  Hwever, due t the pr quality f suchtranslatins, it was cncluded in 1966 that machinetranslatin was nt pssible (ALPAC reprt) and theprject was clsed. They returned t the prblems fmachine translatin nly 10 years later in Japan andEurpe, and 10 years later, after the first successes in 5ynthessiC,enerednthe field f cmputatinal linguistics and artificialintelligence, there was an pprtunity t createcmmercial systems f machine translatin thatTlIsiimsP3allw: 1) written and ral autmated translatin f language pairs amng a large number f languages (Prmt, integrated translatin systems with elements f machine translatin SDL Trads, MemQ,Acrss, DejaVu); 2) diversificatin f specialist Sane *wage Tregel LseLege 1wrk (eg, prject management in integrated translatin systems; creatin f dcuments by Fig. 1. Stages f peratin f machine translatin technical editrs; translatin f text in SGML, XML, systems [8] etc.; language cntrl mdules, fr example,DUDEN-Krrektr; autmated terminlgy In 1976, the US-based platfrm SYSTRAN [9] management, e.g., Multiterm in SDL Trads r was acquired t launch research prjects in the field TermStar in Transit). V erbmbil prject res U f machine translatin in sme develped Eurpean the pssibilities f autmatic translatin. cuntries such as Germany, England, France, Italy which was created t translate Russian-English texts frmer in the military industry and NASA. Based n this frmalizing the f the platfrm a system f ine translatin was createdtranslatr fr pairs f languages . Sincef secnd is • has been used in the Institutes f Germany and Canada, and fr tw years it has been crpra the basis f the JAP-EN, EN-JAP machine [5; 6] translatin system. The linguistic aspects f machine translatin Based n the SYSTRAN platfrm, an systems are based n such mdules as 1) internatinal EURTRA prject was created inmrphlgical (lemmatizatin f lexical units, 1978, with the participatin f wrking grups f search f lexical units in the dictinary, analysis f researchers frm Grenble, Pisa, Saarbriicken, mrphemes, recgnitin f cntext grammatical Manchester, etc. The prject created a prttype class f lexical units, distinctins, flexins, etc.); 2) machine translatin system fr 9 wrking languagessyntactic (recgnitin f types f syntactic f the Eurpean Unin (72 pairs f languages),structures, relatinal relatinships between which wrked n a limited vcabulary — 20,000 individual elements f syntactic structure, etc.); 3) vcabulary articles in the telecmmunicatinssemantic (separatin f the lexical meaning f industry. Fr the interpretatin f dictinaries and multivalued lexical units and affixes, definitin f grammatical rules, the system architecture was their semantic functin, synthesis f their syntactic created based n the lgical prgramming language uniqueness based n semantic analysis). Thus, the f the predictrs f the mathematical lgic f Hrn's sequential stages f the peratin f machine dispsitins — Prlgue [10]. This prject became the translatin systems are analysis, transfer, synthesis, prttype f the fllwing prjects, a detailedand interlingua [7], which can be schematically descriptin f which des nt allw the scpe f thisillustrated in Figs. 1: article.The mst famus experiment in the field fmachine translatin f verbal natural language, als used frfunded by the German Federal Ministry f Researchand Technlgy (BMFT), knwn as Verhmuhil(DFKI , Saarbr ficken, 1993-2000) . The purpse f theprject was t create an autmated ral translatinsystem (DE, ENG, JAP) fr translating simple dialgs int cnversatinal tpics such as "Htel", "Appintment", "Getting Help", "Travel Planning" [8]. and mre. T create the system, 10,000 f the mst Cntrlled languages allw the use f additinal cmmn German language tkens have been tls ptimize the perfrmance f technical islated, but individual features f speech, editrs intnatin, prnunciatin, etc. have hindered the verificatin prgrams and • translatin successful implementatin f the prject and have prfessinal texts. Sftware fr creating prfessinalbeen discntinued [8]. texts based n a cntrlled language is nt a translatin prgram. The main functin f suchDISCUSSINS prgrams is t mnitr the bservance f the rules f Cntrlled languages based n simple syntax "crprate" language, fr example, autmatic search cnstructins, the predminant use f ne-t-ne fr "unauthrized" syntax cnstructins, terms and tkens, unique synnyms and antnyms, and high- term elements, vilatins f stylistic characteristicsquality autmated analysis + text synthesis allw f the text, autmatic ntificatin f "errr" and greatly imprve the quality f machine translatin alternative suggestins fr its eliminatin (CLAT[11]. technlgy [12]; [13]; [14], Acrcheek). Cntrlled languages are nt artificial languages Let us dwell n the functinal cmpnents f like Esperant, but natural simplified languages, CLAT (Cntrlled Language Authring Technlgy) artificially created based n a limited amunt f technlgy. CLAT technlgy cnsists f a CLAT vcabulary, grammatical and syntactic structures. server and a CLAT client that can functin The first cntrlled language, numbered 850, was i pendently (Java CLAT-Client) r in a text editr created based n English in 1930 (Basic English). In ( fr and the 70s f the twentieth century with the nset f the glbalizatin prcess, cntrlled languages began t CiAT•Cherts CLAT-Selver M IT be generated as "crprate" languages t create and Jiw CLAT-Client UmphlaMMI,imprve the quality f technical dcumentatin, asCLAT-In fr %WIwell as t save mney n its translatin. This isachieved thrugh the cmprehensin f prfessinaltexts, standardizatin f terminlgy, and imprvedquality f machine translatin.The standard fr creating such languages was Fig. I. Cmpnents f technlgy [13].initiated by Caterpillar's Caterpillar FundamentalEnglish (CFE). In 1986, ASD Simplified English CLAT allws technical editrs t create flawless,(800 lexical units) was develped t meet the sequential prfessinal texts thrugh integratedspecific needs f the aircraft and rcket industry. punctuatin and spelling mdules (extra spaces,Tday, mst internatinal crpratins create unnecessary r missing punctuatin, ldInewtrlled languages t meet internal needs, eg, spelling, capitelwercase, cmpsing tgether: individually I hyphen, typgraphical errrs). Wrd brders are punctuated by spaces r spaces, andDefense sentence bundaries, unless it is a structured file-language (SGML-IXML), are punctuated and etc.). Errr messages are displayed in different Wrd capitalized. Each sentence is decmpsed int text editr windws: separate clauses f the sentence, depending n the psitin f the verb is determined by the type f gmr. vim L am F.1 .1.12.1 m sentence (narrative, interrgative, infinitive, etc.). At fIWRIelRfl b .f it ■ nal ann. Ivan fan , a aanrala:pla e ala-n la re 114 1 , :f the heart f the CLAT cntrl functin is the crrect laa ernnlx a all a xe ne... M m eet a , cknallantuftde net4Nen Innen L.. n .e eenSuletnencrmin..nald ann , bn mina Mallbana W aal. Irian definitin f the sentence members. Salta HE 'en ILneeenannr e fen t In rmaa. .mennt-er Vatnten...1:thennen bn .•Cetrwr411,,te,r Each wrd is analyzed by the prgram as a VwerEffiamh• V M linguistic categry: a grammatical class (nun, verb, DI., 'au •iLinal a. r D.adjective, article) is defined. Fr


  • Impact of poor water and sanitation on household economy was investigated.
  • Qualitative and quantitative approaches were used to assess the impact of poor water and sanitation on households economy.
  • People have not access to proper sanitation facilities in most of the rural settlements .
  • The diseases ratio was very high because of poor water and sanitation condition.
  • Unawareness of WATSAN related diseases affect adversely on household economy by hospitalization, transportation and medical costs.