Transliterations play an important role in multilingual entity reference resolution, because proper names increasingly travel between languages in news and social media. Previous work associated with machine translation targets transliteration only single between language pairs, focuses on specific classes of entities (such as cities and celebrities) and relies on manual curation, which limits the expression power of transliteration in multilingual environment. By contrast, we present an unsupervised transliteration model covering 69 major languages that can generate good transliterations for arbitrary strings between any language pair. Our model yields top-(1, 20, 100) averages of (32.85 , 60.44 , 83.20 ) in matching gold standard transliteration compared to results from a recently-published system of (26.71 , 50.27 , 72.79 ). We also show the quality of our model in detecting true and false friends from Wikipedia high frequency lexicons. Our method indicates a strong signal of pronunciation similarity and boosts the probability of finding true friends in 68 out of 69 languages.
Transliteration research first associates with the field of orthographic similarity detections since sound similarities co-exist with orthographic similarities @cite_18 @cite_10 @cite_3 @cite_5 @cite_24 @cite_9 . This work shows reasonableness of character-based transliteration between close languages (i.e. languages sharing characters) but does not discuss on distant language pairs.
@cite_18 @cite_9 @cite_3 @cite_24 @cite_5 @cite_10
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In this article we want to investigate the semantic (figurative) structures that underlie false friends, especially semantic false friends, in various European languages (Spanish, French, German and English). Chance false friends share the same form but have different etymologies and different meanings in different languages. They can be compared to homonyms in a single natural language. Semantic false friends, by contrast, have the same etymological origin, their meanings differ in different language, but one can still detect semantic relations between them. They can be considered to be cross-linguistic equivalents to polysemous words in a single natural language. The links between their meanings in different languages can be based on metaphor, metonymy and euphemism, but also on specialisation and generalisation. Semantic false friends are the semantic relics of pragmatic language use over time and space. Studying false friends is, however, more than an exercise in diachronic pragmatics. It has important implications for translation and cross-linguistic communication, where an awareness of false friends is important together with knowledge of certain pragmatic strategies, which help to avoid misunderstandings or mistranslations. The study of the underlying figurative links between false friends also adds a new dimension to cognitive semantics. Abstract In two experiments Dutch–English bilinguals were tested with English words varying in their degree of orthographic, phonological, and semantic overlap with Dutch words. Thus, an English word target could be spelled the same as a Dutch word and or could be a near-homophone of a Dutch word. Whether such form similarity was accompanied with semantic identity (translation equivalence) was also varied. In a progressive demasking task and a visual lexical decision task very similar results were obtained. Both tasks showed facilitatory effects of cross-linguistic orthographic and semantic similarity on response latencies to target words, but inhibitory effects of phonological overlap. A third control experiment involving English lexical decision with monolinguals indicated that these results were not due to specific characteristics of the stimulus material. The results are interpreted within an interactive activation model for monolingual and bilingual word recognition (the Bilingual Interactive Activation model) expanded with a phonological and a semantic component. A series of progressive demasking and lexical decision experiments investigated how the recognition of target words exclusively belonging to one language is affected by the existence of orthographic neighbors from the same or the other language of bilingual participants. Increasing the number of orthographic neighbors in Dutch systematically slowed response times to English target words in Dutch English bilinguals, while an increase in target language neighbors consistently produced inhibitory effects for Dutch and facilitatory effects for English target words. Monolingual English speakers also showed facilitation due to English neighbors, but no effect of Dutch neighbors. The experiments provide evidence for parallel activation of words in an integrated Dutch English lexicon. An implemented version of such a model making these assumptions, the Bilingual Interactive Activation (BIA) model, is shown to account for the overall pattern of results. © 1998 Academic Press One of the striking features of bilingual language performance is the apparent ease with which the bilingual manages to keep interference from the non-target language at a minimal level. The fact remains, however, that interference from one language to the other does occur and is observable with respect to both language structure and linguistic processing. For example, in language production, interference from the first language can be noticed both at the phonological level (foreign accents) and at the sentence level (borrowed syntax), as well as in intrusions of words from the other language (accidental lexical borrowings). This paper presents a method for inducing translation lexicons based on transduction models of cognate pairs via bridge languages. Bilingual lexicons within languages families are induced using probabilistic string edit distance models. Translation lexicons for arbitrary distant language pairs are then generated by a combination of these intra-family translation models and one or more cross-family on-line dictionaries. Up to 95 exact match accuracy is achieved on the target vocabulary (30-68 of inter-family test pairs). Thus substantial portions of translation lexicons can be generated accurately for languages where no bilingual dictionary or parallel corpora may exist.