Sammenlign metoder

	Klassifisering av dialoghandlinger	Sentimentanalyse	Sporutfylling
Fagfelt	Tekstutvinning	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Opprinnelsesår	1997–2000	—	2018 (joint slot-gate model); BIO tagging foundations earlier
Opphavsperson	Stolcke et al.; Jurafsky et al.	—	Established via NER/IOB tagging literature; popularised for dialogue by Goo et al. (2018) and Chen et al. (2019)
Type	NLP utterance-classification task	NLP text-classification task	NLP token-classification / information-extraction task
Opprinnelig kilde	Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Goo, C.W., Gao, G., Hsu, Y.K., Huo, C.L., Chen, T.C., Hsu, S.C., & Chen, Y.N. (2018). Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. Proceedings of NAACL-HLT 2018.
Alias	dialogue act tagging, speech act classification, Diyalog Eylem Sınıflandırma (Dialogue Act Classification)	opinion mining, polarity detection, duygu analizi	slot doldurma, Slot Doldurma (Slot Filling / NER-NLU), information slot extraction, dialogue slot filling
Relaterte	4	3	5
Sammendrag	Dialogue act classification is a natural-language-processing task that automatically labels the communicative function of each utterance in a conversation — such as question, answer, greeting, or rejection. Consolidated by Jurafsky et al. (1997) and Stolcke et al. (2000), it is a foundational component for chatbots and discourse analysis.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	Slot filling is a natural-language-understanding task that extracts predefined template fields — such as date, location, or product name — from a user utterance. It emerged as a core component of dialogue systems and form-based information extraction, and became widely studied after Goo et al. (2018) introduced the Slot-Gated Model for joint slot filling and intent prediction, followed by Chen et al. (2019) who extended the paradigm with BERT-based joint modelling.