Named entity recognition (NER) from speech is usually made through a pipeline process that consists in (i) processing audio using an automatic speech recognition system (ASR) and (ii) applying a NER to the ASR outputs. The latest data available for named entity extraction from speech in French were produced during the ETAPE evaluation campaign in 2012. Since the publication of ETAPE’s campaign results, major improvements were done on NER and ASR systems, especially with the development of neural approaches for both of these components. In addition, recent studies have shown the capability of End-to-End (E2E) approach for NER / SLU tasks. In this paper, we propose a study of the improvements made in speech recognition and named entity recognition for pipeline approaches. For this type of systems, we propose an original 3-pass approach. We also explore the capability of an E2E system to do structured NER. Finally, we compare the performances of ETAPE’s systems (state-of-the-art systems in 2012) with the performances obtained using current technologies. The results show the interest of the E2E approach, which however remains below an updated pipeline approach.

This paper describes the TALN/LS2N system participation at the Building and Using Comparable Corpora (BUCC) shared task. We first introduce three strategies: (i) a word embedding approach based on fastText embeddings; (ii) a concatenation approach using both character Skip-Gram and character CBOW models, and finally (iii) a cognates matching approach based on an exact match string similarity. Then, we present the applied strategy for the shared task which consists in the combination of the embeddings concatenation and the cognates matching approaches. The covered languages are French, English, German, Russian and Spanish. Overall, our system mixing embeddings concatenation and perfect cognates matching obtained the best results while compared to individual strategies, except for English-Russian and Russian-English language pairs for which the concatenation approach was preferred.