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Type or paste a Hindi text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Hindi Text for POS Analysis |
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भारत के प्रधानमंत्री नरेंद्र मोदी ने नई दिल्ली में आयोजित एक शिखर सम्मेलन में भाग लिया। इस बैठक में रिलायंस इंडस्ट्रीज और टाटा समूह के प्रतिनिधियों ने भी शिरकत की।
कल रात ठंडी हवाएँ चल रही थीं, इसलिए छोटी लड़कियाँ अपने कमरों में सो रही थीं।
राम ने रावण को बाण से मारा और विभीषण को लंका का राजा बनाया।
आजकल बहुत से लोग ऑनलाइन शॉपिंग करना पसंद करते हैं क्योंकि यह बहुत सुविधाजनक है। क्या आपने अपना पासवर्ड रिसेट किया?
A part of speech is a category that describes the role a word plays in a sentence.
Improving Hindi language learning using Part-of-Speech (POS) tagging involves leveraging syntactic and morphological information to understand sentence structure, disambiguate word meanings, and master inflectional rules.
- Hindi Part-of-Speech
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UPOS of Hindi
UPOS (Universal POS) is a Coarse-grained and simplified tag that work consistently across all languages. They are shown in the following format.
Headword lemma UPOS DEP 👤NER
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XPOS of Hindi
XPOS (Detailed POS) is a Fine-Grained tag specific to the Hindi language and the Hindi training data. They are shown in the following format.
Headword lemma XPOS DEP 👤NER
Headword : Headwords are displayed in bold.
lemma : The dictionary form or "root" of a Hindi word. It removes grammatical variations. The lemma is only displayed if the headword is not equal to the lemma.
UPOS : Universal Part-of-Speech. A coarse-grained, standardized tag (like NOUN, VERB, or ADJ) designed to work across all human languages. See examples
XPOS : Language-Specific Part-of-Speech. A fine-grained tag specific to a particular Hindi language’s grammar (e.g., distinguishing a plural noun from a singular noun, etc). See examples
DEP : Dependency. The grammatical relationship between words. It shows how words depend on one another, such as identifying which word is the subject (nsubj) or the direct object (obj). See examples
👤NER : Named Entity Recognition. The identification of ""real-world"" entities within the text, such as People (PER), Locations (GPE), Organizations (ORG), or Dates. See examples
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