Named Entity Recognition for Spoken Finnish

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

4 Sitaatiot (Scopus)
154 Lataukset (Pure)


In this paper we present a Bidirectional LSTM neural network with a Conditional Random Field layer on top, which utilizes word, character and morph embeddings in order to perform named entity recognition on various Finnish datasets. To overcome the lack of annotated training corpora that arises when dealing with low-resource languages like Finnish, we tried a knowledge transfer technique to transfer tags from Estonian dataset. On the human annotated in-domain Digitoday dataset, out system achieved F1 score of 84.73. On the out-of-domain Wikipedia set we got F1 score of 67.66. In order to see how well the system performs on speech data, we used two datasets containing automatic speech recognition outputs. Since we do not have true labels for those datasets, we used a rule-based system to annotate them and used those annotations as reference labels. On the first dataset which contains Finnish parliament sessions we obtained F1 score of 42.09 and on the second one which contains talks from Yle Pressiklubi we obtained F1 score of 74.54.

OtsikkoAI4TV 2020 - Proceedings of the 2nd International Workshop on AI for Smart TV Content Production, Access and Delivery
ISBN (elektroninen)9781450381468
DOI - pysyväislinkit
TilaJulkaistu - 12 lokak. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Workshop on AI for Smart TV Content Production, Access and Delivery - Virtual, Online, Yhdysvallat
Kesto: 12 lokak. 202012 lokak. 2020
Konferenssinumero: 2


WorkshopInternational Workshop on AI for Smart TV Content Production, Access and Delivery
KaupunkiVirtual, Online


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