Automatic Identification of False Friends in Parallel Corpora: Statistical and Semantic Approach

False friends are pairs of words in two languages that are perceived as similar but have different meanings. We present an improved algorithm for acquiring false friends from sentence-level aligned parallel corpus based on statistical observations of words occurrences and co-occurrences in the parallel sentences. The results are compared with an entirely semantic measure for cross-lingual similarity between words based on using the Web as a corpus through analyzing the words’ local contexts extracted from the text snippets returned by searching in Google. The statistical and semantic measures are further combined into an improved algorithm for identification of false friends that achieves almost twice better results than previously known algorithms. The evaluation is performed for identifying cognates between Bulgarian and Russian but the proposed methods could be adopted for other language pairs for which parallel corpora and bilingual glossaries are available.

Linguistic Corpora as International Cultural Heritage: The Corpus of Bulgarian and Ukrainian Parallel Texts

The paper relates about our ongoing work on the creation of a corpus of Bulgarian and Ukrainian parallel texts. We discuss some differences in the approaches and the interpretation of some concepts, as well as various problems associated with the construction of our corpus, in particular the occasional ‘nonparallelism’ of original and translated texts. We give examples of the application of the parallel corpus for the study of lexical semantics and note the outstanding role of the corpus in the lexicographic description of Ukrainian and Bulgarian translation equivalents. We draw attention to the importance of creating parallel corpora as objects of national as well as global cultural heritage.

Web-application for Presentation of Bulgarian Language Heritage: Bilingual Digital Corpora and Dictionaries

The paper describes three software packages - the main components of a software system for processing and web-presentation of Bulgarian language resources – parallel corpora and bilingual dictionaries. The author briefly presents current versions of the core components “Dictionary” and “Corpus” as well as the recently developed component “Connection” that links both “Dictionary” and “Corpus”. The components main functionalities are described as well. Some examples of the usage of the system’s web-applications are included.

On a Stochastic Partial Differential Equation with a Noisy Term

2000 Mathematics Subject Classification: 60H15, 60H40

Accent Recognition for Noisy Audio Signals

It is well established that accent recognition can be as accurate as up to 95% when the signals are noise-free, using feature extraction techniques such as mel-frequency cepstral coefficients and binary classifiers such as discriminant analysis, support vector machine and k-nearest neighbors. In this paper, we demonstrate that the predictive performance can be reduced by as much as 15% when the signals are noisy. Specifically, in this paper we perturb the signals with different levels of white noise, and as the noise become stronger, the out-of-sample predictive performance deteriorates from 95% to 80%, although the in-sample prediction gives overly-optimistic results. ACM Computing Classification System (1998): C.3, C.5.1, H.1.2, H.2.4., G.3.