A Parallel E nglish - S erbian - B ulgarian - M acedonian Lexicon of Named Entities (2022)

Abstract: This paper describes the creation of a parallel multilingual lexicon of named entities from English to three South Slavic languages: Serbian, Bulgarian and Macedonian, with Wikipedia as a source. The basics of the proposed methodology are well known. This methodology provides a cheap opportunity to build multilingual lexicons, without having expertise in target languages. Wikipedia’s database dump can be freely downloaded in SQL and XML formats. The method presented here has been used to build a Python application that extracts the English – Serbian – Bulgarian – Macedonian parallel titles from Wikipedia and classifies them using the English Wikipedia category system. The extracted named entity sets have been classified into five classes: PERSON, ORGANIZATION, LOCATION, PRODUCT, and MISC (miscellaneous). It has been achieved using Wikipedia metadata. The quality of classification has been checked manually on 1,000 randomly chosen named entities. The following are the results obtained: 97% for precision and 90% for recall.

## introduction wikipedia is a free online encyclopedia, made and maintained as an open coordinated effort venture by a network of volunteer editors, utilizing a wiki -based editing system. hosted and supported by the wikimedia foundation, since its start in 2001, the site has grown in both popularity and size. at the time of writing this paper (march 2022), wikipedia contained over 58 million articles in 323 languages; its english version has over 6 million articles. the richness of information and texts continuously makes it an object of special research interest among the nlp (natural language processing) community. by attracting approximately 6 billion visitors per month @xcite , it is the largest and most popular general reference work on the world wide web. the term named entity (ne) refers to expressions describing objects, like persons, locations, and organizations. it was first introduced to the nlp community at the end of the 20th century. named entities are often denoted by proper names. they can be abstract or have a physical existence. some other expressions, describing money, percentage, time, and date might also be considered as named entities. examples of named entities include united states, paris, google, mercedes benz, microsoft windows, or anything else that can be named. the role of named entities has become more and more important in nlp. their information is crucial in information extraction. as recent systems mostly rely on machine learning techniques, their performance is based on the size and quality of given training data. this data is expensive and cumbersome to create because experts usually annotate corpora manually to achieve high-quality data. as a result, these data sets often lack coverage, are not up to date, and are not available in many languages. to overcome this problem, semi -automatic methods for resource construction from other available sources were deployed. even though wikipedia isn't made and maintained by linguists, metadata about articles, for instance, translations, disambiguations, or categorizations are accessible. its structural features, size, and multilingual availability give a reasonable base to derive specialized resources, like multilingual lexicons @xcite . researchers have found that around 74% of wikipedia pages describe named entities @xcite , a clear indication of wikipedia's high coverage for named entities. each wikipedia article associated with a named entity is identified with its title, which is itself a named entity. that is a perfect opportunity to build parallel lexicons of named entities between them. ## related work building multilingual lexicons from wikipedia has been a subject of research for more than 10 years. schönhofen et al. @xcite exploited wikipedia hyperlinkage for query term disambiguation. tyers and pienaar @xcite described a simple, fast, and computationally inexpensive method for extracting bilingual dictionary entries from wikipedia (using the interwiki link system) and assessed the performance of this method with respect to four language pairs. yu and tsujii @xcite proposed a method using the interlanguage link in wikipedia to build an english-chinese lexicon. knopp @xcite showed how to use the wikipedia category system to classify named entities. @xcite described how to use wikipedia contents to automatically generate a lexicon of named entities and synonyms that are all referring to the same entity. halek et al. @xcite attempted to improve machine translation from english of named entities by using wikipedia. in @xcite , the author evaluated a bilingual bidirectional english-russian dictionary created from wikipedia article titles. higashinaka et al. @xcite aimed to create a lexicon of 200 extended named entity (ene) types, which could enable fine-grained information extraction. oussalah and mohamed @xcite demonstrated how to use info-boxes in order to identify and extract named entities from wikipedia. ## method wikipedia's database dump can be freely downloaded in sql and xml formats @xcite . the method presented here has been used to build a python application that extracts the english -serbian -bulgarian -macedonian parallel titles from wikipedia and classifies them using the english wikipedia category system. the flowchart presented in figure 1 shows the process used for building the lexicon. 1. extract parallel titles with english as a first language -for building multilingual lexicons, two tables from the database are necessary: table of pages and table of interlanguage links. the page table is the "core of the wiki". it contains titles and other essential metadata for different wikipedia namespaces. the interlanguage links table contains links between pages in different languages. using these two tables, it is an easy programming task to create huge multilingual dictionaries without having any language expertise. in case of building multilingual lexicons with more than one language besides english, a new entry is created if there is a match between english and at least one of the other languages. location -geographical terms, fictional places, cosmic terms; product -industrial products, software products, weapons, artworks, documents, concepts, standards, laws, formats, anthems, algorithms, journals, coats of arms, platforms, websites; misc -events, languages, peoples, tribes, alliances, orders, scientific discoveries, theories, titles, currencies, holidays, dynasties, positions, projects, historical periods, battles, competitions, alliances, deceases, programs, set of locations, awards, musical genres, missions, artistic directions, sets of organizations, networks. the method presented in previous chapter has been used to build a python application which extracts title sets independently on the languages. this program was applied to the wikipedia database to extract the english -serbian -bulgarian -macedonian sets of named entities. the result of the extraction after the first two steps from figure 1 was 586,355 entries for english, 374,691 for serbian, 258,940 for bulgarian, and 149,633 for macedonian. there are few titles in all south slavic wikipedias that are written in the original language (mostly english). in addition to that, there are few titles in serbian wikipedia in the latin alphabet. the transliteration from latin to cyrillic and vice versa in serbian is relatively straightforward. ## utilization lexicons, like the one presented in this paper, can be used in machine translation (mt). most statistical mt systems do not deal explicitly with named entities, simply relying on the model of selecting the correct translation, i.e., mistranslating them as generic nouns. it is also possible that, when not identified, named entities may be left out of the output translation, which also has implications for the readability of the text. because most nes are rare in texts, statistical mt systems are not capable of producing quality translations for them. another problem with mt systems is that failure to recognize nes often harms the morpho -syntactic and lexical context outside of nes itself. if named entities are not immediately identified, certain morphological features of adjacent and syntactically related words, as well as word order, may be incorrect. however, developers of commercial mt systems often do not pay enough attention to the correct automatic identification of certain types of ne, e.g. names of organizations. this is partly due to the greater complexity of this problem (the set of proper nouns is open and very dynamic), and partly due to lack of time and other development resources. one solution to this problem is using a parallel lexicon of named entities. if the lexicon contains a translation of the named entity, the translation quality will probably be good. ## conclusion