Publication
Multilingual Annotation and Disambiguation of Discourse Connectives for Machine Translation
Abstract
Many discourse connectives can signal several types of relations between sentences. Their automatic disambiguation, i.e. the labeling of the correct sense of each occurrence is important for discourse parsing, but could also be helpful to machine translation. We describe new approaches for improving the accuracy of manual annotation of three discourse connectives (two English, one French) by using parallel corpora. An appropriate set of labels for each connective can be found using information from their translations. Our results for automatic disambiguation are state-of-the-art, at up to 85% accuracy using surface features. Using feature analysis, contextual features are shown to be useful across languages and connectives.
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Ontological neighbourhood
Related concepts (30)
Word-sense disambiguation
Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.
Text corpus
In linguistics and natural language processing, a corpus (: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. In search technology, a corpus is the collection of documents which is being searched.
Machine translation
Machine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.
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