เปรียบเทียบวิธี

	การจำแนกความหมายของคำ	การรู้จำหน่วยคำนาม (Named Entity Recognition - NER)	การวิเคราะห์ความรู้สึก
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	2009	—	—
ผู้ริเริ่ม	Navigli (survey, 2009)	—	—
ประเภท	NLP semantic-disambiguation task	NLP sequence-labelling task	NLP text-classification task
แหล่งต้นตำรับ	Navigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Computing Surveys (CSUR), 41(2), Article 10, 1-69.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
ชื่อเรียกอื่น	WSD, sense tagging, Sözcük Anlamı Belirsizlik Giderme (WSD)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	opinion mining, polarity detection, duygu analizi
ที่เกี่ยวข้อง	2	3	3
สรุป	Word sense disambiguation (WSD) is the natural-language-processing task of choosing the correct meaning of a polysemous word from its context. Surveyed by Navigli (2009), it resolves which sense of a many-meaning word applies in a given sentence, improving the quality of information retrieval, machine translation, and question answering.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.	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.