Comparable corpora and computer-assisted translation / Estelle Maryline Delpech.
Computer-assisted translation (CAT) has always used translation memories, which require the translator to have a corpus of previous translations that the CAT software can use to generate bilingual lexicons. This can be problematic when the translator does not have such a corpus, for instance, when t...
Saved in:
| Online Access: |
Full Text (via ProQuest) |
|---|---|
| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
London :
ISTE,
2014.
|
| Series: | Cognitive science and knowledge management series.
|
| Subjects: |
Table of Contents:
- Cover; Title Page; Copyright; Contents; Acknowledgments; Introduction; PART 1: Applicative and Scientific Context; Chapter 1: Leveraging Comparable Corpora for Computer-assisted Translation ; 1.1. Introduction; 1.2. From the beginnings of machine translation to comparable corpora processing; 1.2.1. The dawn of machine translation; 1.2.2. The development of computer-assisted translation; 1.2.3. Drawbacks of parallel corpora and advantages of comparable corpora; 1.2.4. Difficulties of technical translation; 1.2.5. Industrial context.
- 1.3. Term alignment from comparable corpora: a state-of-the-art1.3.1. Distributional approach principle; 1.3.2. Term alignment evaluation; 1.3.2.1. Precision at rank N or TopN; 1.3.2.2. MRR; 1.3.2.3. MAP; 1.3.3. Improvement and variants of the distributional approach; 1.3.3.1. Favoring distributional symmetry; 1.3.3.2. Using syntactic contexts; 1.3.3.3. Relying on trusted elements; 1.3.3.4. Improving semantic information representation; 1.3.3.5. Using second-order semantic affinities; 1.3.3.6. Improving the bilingual resource with semantic classes; 1.3.3.7. Translating polylexical units.
- 1.3.4. Influence of data and parameters on alignment quality1.3.4.1. Data; 1.3.4.2. Parameters; 1.3.5. Limits of the distributional approach; 1.4. CAT software prototype for comparable corpora processing; 1.4.1. Implementation of a term alignment method; 1.4.1.1. Implementation and data; 1.4.1.2. Extraction of the terms to be aligned; 1.4.1.3. Collecting context vectors; 1.4.1.3.1. Monolexical term context vectors; 1.4.1.4. Polylexical term context vectors; 1.4.1.5. Translation of the source context vectors; 1.4.1.6. Term alignment; 1.4.2. Terminological records extraction.
- 1.4.3. Lexicon consultation interface1.5. Summary; Chapter 2: User-Centered Evaluation of Lexicons Extracted from Comparable Corpora; 2.1. Introduction; 2.2. Translation quality evaluation methodologies; 2.2.1. Machine translation evaluation; 2.2.1.1. Automatic evaluation measures; 2.2.1.2. Human MT evaluation; 2.2.2. Human translation evaluation; 2.2.2.1. Quantitative models; 2.2.2.2. Non-quantitative models; 2.2.3. Discussion; 2.3. Design and experimentation of a user-centered evaluation; 2.3.1. Methodological aspects; 2.3.1.1. Evaluation criteria and purpose.
- 2.3.1.2. Subject matter expertise2.3.1.3. Basis for comparison; 2.3.2. Experimentation protocol; 2.3.2.1. Data; 2.3.2.1.1. Comparable corpora and extracted lexica; 2.3.2.1.2. Texts to be translated; 2.3.2.1.3. Resources used in the translation situation; 2.3.2.1.4. Translators and judges; 2.3.2.2. Evaluation progress; 2.3.2.2.1. Translation phase; 2.3.2.2.2. Translation quality evaluation phase; 2.3.3. Results; 2.3.3.1. Lexicons usability; 2.3.3.1.1. Translation speed; 2.3.3.1.2. Use of resources; 2.3.3.1.3. Translators' impressions on the lexicons extracted from comparable corpora.