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| Poängsättning av textkoherens – Modellering av lokal koherens× | Textklassificering× | |
|---|---|---|
| Ämnesområde | Textutvinning | Textutvinning |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2008 | — |
| Upphovsperson≠ | Barzilay & Lapata | — |
| Typ≠ | NLP text-level scoring task | Supervised NLP classification task |
| Ursprungskälla≠ | Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34. DOI ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| Alias≠ | coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması | text categorization, document classification, topic classification, metin sınıflandırma |
| Närliggande | 4 | 4 |
| Sammanfattning≠ | Text coherence scoring computes a document-level coherence score with machine learning, rooted in the entity-based local coherence model introduced by Barzilay and Lapata (2008). It measures how well the sentences of a text hang together, using either an entity-grid model, a graph-based approach, or a transformer-based model. | 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. |
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