Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Filloqëzimi i kuptimit të fjalës (WSD)× | Njohja e Entiteteve të Emërtuara (NER)× | Analiza e ndjenjave× | |
|---|---|---|---|
| Fusha | Nxjerrja e tekstit | Nxjerrja e tekstit | Nxjerrja e tekstit |
| Familja | Process / pipeline | Process / pipeline | Process / pipeline |
| Viti i origjinës≠ | 2009 | — | — |
| Krijuesi≠ | Navigli (survey, 2009) | — | — |
| Lloji≠ | NLP semantic-disambiguation task | NLP sequence-labelling task | NLP text-classification task |
| Burimi themelues≠ | Navigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Computing Surveys (CSUR), 41(2), Article 10, 1-69. DOI ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Emërtime të tjera | WSD, sense tagging, Sözcük Anlamı Belirsizlik Giderme (WSD) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) | opinion mining, polarity detection, duygu analizi |
| Të lidhura≠ | 2 | 3 | 3 |
| Përmbledhja≠ | 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. |
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