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.
Similarly, work on cognate identification also focus on close language pairs @cite_16 @cite_0 @cite_15 @cite_13 @cite_19 @cite_6 @cite_30 . However, we believe multilingual transliterations contribute to even distant languages (e.g. English and Japanese) when handling OOV words and resolving ambiguities.
@cite_30 @cite_6 @cite_0 @cite_19 @cite_15 @cite_16 @cite_13
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This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness. This paper 1 presents DISCO, a tool for retrieving the distributional similarity be- tween two given words, and for retrieving the distributionally most similar words for a given word. Pre-computed word spaces are freely available for a number of languages including English, German, French and Italian, so DISCO can be used off the shelf. The tool is imple- mented in Java, provides a Java API, and can also be called from the command line. The per- formance of DISCO is evaluated by measuring the correlation with WordNet-based semantic similarities and with human relatedness judgements. The evaluations show that DISCO has a higher correlation with semantic similarities derived from WordNet than latent semantic analysis (LSA) and the web-based PMI-IR. The coinciding form and meaning similarity of cognates, e.g. ‘flamme’ (French), ‘Flamme’ (German), ‘vlam’ (Dutch), meaning ‘flame’ in English, facilitates learning of additional languages. The cross-language frequency and similarity distributions of cognates vary according to evolutionary change and language contact. We compare frequency and orthographic (O), phonetic (P), and semantic similarity of cognates, automatically identified in semi-complete lexicons of six widely spoken languages. Comparisons of P and O similarity reveal inconsistent mappings in language pairs with deep orthographies. The frequency distributions show that cognate frequency is reduced in less closely related language pairs as compared to more closely related languages (e.g., French-English vs. German-English). These frequency and similarity patterns may support a better understanding of cognate processing in natural and experimental settings. The automatically identified cognates are available in the supplementary materials, including the frequency and similarity measurements. A pair of pressure sensing probe tubes are placed in the fan discharge of a turbofan engine, aft of the outlet guide vanes (OGVs) the tubes being circumferentially spaced by a fraction of the OGV pitch. A comparing means receives both pressures sensed by the double-barrelled probes and aerodynamically actuates to automatically select the higher of the two pressures. An accurate pressure indication representative of the mainstream pressure is therefore obtained despite the localized lower pressure turbulence area that may be caused by the presence of the upstream outlet guide vane(s). When participants are asked to translate an ambiguous word, they are slower and less accurate than in the case of single-translation words (e.g., Laxen & Lavour, 2010; Tokowicz & Kroll, 2007). We report an experiment to further examine this multiple-translation effect by investigating the influence of variables shown to be relevant in bilingual processing. The experiment included cognates and non-cognates with one translation or with multiple translations. The latter were presented with their dominant or subordinate translations. Highly-proficient balanced bilinguals responded to a translation recognition task in the two language directions (Catalan–Spanish and Spanish–Catalan). The results showed a significant multiple-translation effect in both cognates and non-cognates. Moreover, this effect was obtained regardless of language dominance and translation direction. Participants were faster and more accurate when performing translation recognition for the dominant than for the subordinate translations. The findings are interpreted adopting the Distributed Representation Model (de Groot, 1992b).