Methoden vergleichen

	Dialogaktklassifikation	Sentiment-Analyse	Slot Filling	Textklassifizierung
Fachgebiet	Text Mining	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	1997–2000	—	2018 (joint slot-gate model); BIO tagging foundations earlier	—
Urheber	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)	—
Typ	NLP utterance-classification task	NLP text-classification task	NLP token-classification / information-extraction task	Supervised NLP classification task
Wegweisende Quelle	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasnamen	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	text categorization, document classification, topic classification, metin sınıflandırma
Verwandt	4	3	5	4
Zusammenfassung	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.