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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.
|Title of host publication||AI4TV 2020 - Proceedings of the 2nd International Workshop on AI for Smart TV Content Production, Access and Delivery|
|Number of pages||5|
|Publication status||Published - 12 Oct 2020|
|MoE publication type||A4 Article in a conference publication|
|Event||International Workshop on AI for Smart TV Content Production, Access and Delivery - Virtual, Online, United States|
Duration: 12 Oct 2020 → 12 Oct 2020
Conference number: 2
|Workshop||International Workshop on AI for Smart TV Content Production, Access and Delivery|
|Period||12/10/2020 → 12/10/2020|
- named entity recognition
- speech recognition
FingerprintDive into the research topics of 'Named Entity Recognition for Spoken Finnish'. Together they form a unique fingerprint.
01/01/2020 → 31/12/2022
Project: Academy of Finland: Other research funding