Publication - Pattern Matching-Based System for Machine Translation (MT)

Pattern Matching-Based System for Machine Translation (MT)

Research Area:  
In Proceedings


Year: 2006
Authors: George Tambouratzis; Sokratis Sofianopoulos; Vassiliki Spilioti; Marina Vassiliou; Olga Yannoutsou; Stella Markantonatou
Editor: G. Antoniou; G. Potamias; C. Spyropoulos, D. Plexousakis
Volume: 3955
Book title: Advances in Artificial Intelligence: 4th Hellenic Conference on AI
Series: Lecture Notes in Computer Science
Pages: 345-355
Address: Heraklion, Greece
Organization: Hellenic Artificial Intelligence Society (EETN)
Date: May 18-20
ISBN: 3-540-34117-X
DOI: 10.1007/11752912_35
The innovative feature of the system presented in this paper is the use of pattern-matching techniques to retrieve translations resulting in a flexible, language-independent approach, which employs a limited amount of explicit a priori linguistic knowledge. Furthermore, while all state-of-the-art corpus-based approaches to Machine Translation (MT) rely on bitexts, this system relies on extensive target language monolingual corpora. The translation process distinguishes three phases: 1) pre-processing with ‘light’ rule and statisticsbased NLP techniques 2) search & retrieval, 3) synthesising. At Phase 1, the source language sentence is mapped onto a lemma-to-lemma translated string. This string then forms the input to the search algorithm, which retrieves similar sentences from the corpus (Phase 2). This retrieval process is performed iteratively at increasing levels of detail, until the best match is detected. The best retrieved sentence is sent to the synthesising algorithm (Phase 3), which handles phenomena such as agreement.