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| Konstituentu parsēšana× | Vārdšķiras atpazīšana (POS Tagging)× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2003 | — |
| Autors≠ | Michael Collins (statistical models, 2003) | — |
| Tips≠ | NLP syntactic-analysis task | NLP sequence-labelling task |
| Pirmavots≠ | Collins, M. (2003). Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics, 29(4), 589-637. DOI ↗ | Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP. link ↗ |
| Citi nosaukumi | phrase-structure parsing, constituent parsing, Kurucu Öbek Ayrıştırma (Constituency Parsing) | part-of-speech tagging, grammatical tagging, Sözcük Türü Etiketleme (POS Tagging) |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Constituency parsing is a natural-language-processing task that represents a sentence as a tree of recursively nested phrase-structure constituents — for example S → NP + VP. Building on the head-driven statistical parsing models introduced by Collins (2003) and the later neural parsers of Kitaev and colleagues (2019), it exposes the hierarchical syntactic skeleton of a sentence for grammatical pattern extraction and grammar research. | Part-of-speech tagging assigns a grammatical category label — noun, verb, adjective, and so on — to every word in a text. It is a foundational natural-language-processing task, formalised as a statistical model by Ratnaparkhi (1996) and packaged into widely used toolkits such as Stanford CoreNLP (Manning et al., 2014), and it serves as a preliminary step for syntactic analysis and information extraction. |
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