A Deterministic Algorithm for Arabic Character Recognition Based on Letter Properties

Handheld devices are flooding the market, and their use is becoming essential among people. Hence, the need for fast and accurate character recognition methods that ease the data entry process for users arises. There are many methods developed for handwriting character recognition especially for Latin-based languages. On the other hand, character recognition methods for Arabic language are lacking and rare. The Arabic language has many traits that differentiate it from other languages: first, the writing process is from right to left; second, the letter changes shape according to the position in the work; and third, the writing is cursive. Such traits compel to produce a special character recognition method that helps in producing applications for Arabic language. This research proposes a deterministic algorithm that recognizes Arabic alphabet letters. The algorithm is based on four categorizations of Arabic alphabet letters. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose

Automatic inference of the temporal location of situations in Chinese text

Chinese is a language that does not have morphological tense markers that provide explicit grammaticalization of the temporal location of situations (events or states). However, in many NLP applications such as Machine Translation, Information Extraction and Question Answering, it is desirable to make the temporal location of the situations explicit. We describe a machine learning framework where different sources of information can be combined to predict the temporal location of situations in Chinese text. Our experiments show that this approach significantly outperforms the most frequent tense baseline. More importantly, the high training accuracy shows promise that this challenging problem is solvable to a level where it can be used in practical NLP applications with more training data, better modeling techniques and more informative and generalizable features.

A corpus based contrastive approach for the analysis of tense and aspect in translation from English into Mandarin Chinese

Die vorliegende Dissertation nimmt eine korpusbasierte kontrastive Analyse von Tempus und Aspekt in der Übersetzung aus dem Englischen ins Mandarin-Chinesische. Ziel der theoretischen Basis dieser Arbeit ist es, die Charakteristika und Übersetzungsäquivalenz von Tempus und Aspekt in beiden Sprachen zu diskutieren und die wichtigsten Fakten zu erläutern. Der empirische Teil dieser Arbeit beginnt mit drei Fragen: 1)Wie und zu welchem Grad werden Tempus und Aspekt des Englischen ins Mandarin-Chinesische übersetzt? 2)Werden übersetzte mandarin chinesische Texte als TT-oriented bzw. ST-oriented eingeordnet oder bietet sich besser ein third code an, um Übersetzungen dieser Art angemessen zu beschreiben? 3)Ist die translation universals als eine linguistische Eigenheit anwendbar? Um die Fragen zu beantworten und die Antworten zu erklären, stelle ich zwei Korpora mit statistische Verfahren aus der Übersetzungswissenschaft und Kontrastiven Linguistik zwischen dem Englischen und Mandarin-Chinesischen bereit