Improved Word Alignment with Statistics and Linguistic Heuristics

We present a method to align words in a bitext that combines elements of a traditional statistical approach with linguistic knowledge. We demonstrate this approach for Arabic-English, using an alignment lexicon produced by a statistical word aligner, as well as linguistic resources ranging from an English parser to heuristic alignment rules for function words. These linguistic heuristics have been generalized from a development corpus of 100 parallel sentences. Our aligner, UALIGN, outperforms both the commonly used GIZA++ aligner and the state-of-theart LEAF aligner on F-measure and produces superior scores in end-to-end sta

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Ulf Hermjakob
• Keywords: EMNLP 2009, Heuristic Alignment Rules, Natural Language Processing, State-of-theart Leaf Aligner, Statistical Word Aligner