Neural Networks Revisited for Proper Name Retrieval from Diachronic Documents

Developing high-quality transcription systems for very large vocabulary corpora is a challenging task. Proper names are usually key to understanding the information contained in a document. To increase the vocabulary coverage, a huge amount of text data should be used. In this paper, we extend the previously proposed neural networks for word embedding models: word vector representation proposed by Mikolov is enriched by an additional non-linear transformation. This model allows to better take into account lexical and semantic word relationships. In the context of broadcast news transcription and in terms of recall, experimental results show a good ability of the proposed model to select new relevant proper names.