🏠 Beranda
Benchmark
📊 Semua Benchmark 🦖 Dinosaurus v1 🦖 Dinosaurus v2 ✅ Aplikasi To-Do List 🎨 Halaman Bebas Kreatif 🎯 FSACB - Showcase Utama 🌍 Benchmark Terjemahan
Model
🏆 Top 10 Model 🆓 Model Gratis 📋 Semua Model ⚙️ Kilo Code
Sumber Daya
💬 Perpustakaan Prompt 📖 Glosarium AI 🔗 Tautan Berguna

Glosarium AI

Kamus lengkap Kecerdasan Buatan

162
kategori
2.032
subkategori
23.060
istilah
📖
istilah

Named Entity Recognition (NER)

A subtask of Natural Language Processing (NLP) that aims to identify and classify predefined entities such as people, organizations, or locations in unstructured text.

📖
istilah

Entity Extraction

The process of identifying and isolating specific structured information (entities) from unstructured textual data to populate a knowledge base.

📖
istilah

Entity Tagging

The action of associating semantic labels (tags) with entities extracted from a text, enabling their classification and use in question-answering systems.

📖
istilah

Entity Dictionary

A database or structured list containing valid entities and their types, used as a reference for recognition and validation in a QA system.

📖
istilah

Supervised Learning for NER

An approach where an NER model is trained on a manually annotated text corpus to learn to recognize and classify entities.

📖
istilah

Sequence-to-Sequence Model (Seq2Seq)

A neural network architecture used for complex NER tasks, processing an input sequence (text) to produce an output sequence (entity labels).

📖
istilah

Contextual Embeddings (ELMo, BERT)

Vector representations of words that capture their meaning based on the surrounding context, significantly improving the accuracy of extracting ambiguous entities.

📖
istilah

Entity Normalization

The process of standardizing extracted entities (e.g., converting 'Tuesday', 'tue.', and 'Tuesday' to a canonical form) to ensure data consistency.

📖
istilah

Entity Linking

Task of connecting a named entity mentioned in a text to a unique entry in a knowledge base (e.g., a DBpedia or Wikidata URI).

📖
istilah

Annotated Corpus

Collection of texts where entities have been previously identified and labeled by humans, serving as ground truth for training and evaluating NER models.

📖
istilah

False Positive in Extraction

Error where the system incorrectly identifies a text segment as a relevant entity, negatively impacting the precision of the question-answering system.

📖
istilah

Extraction Pipeline

Sequential chain of modules (tokenization, NER, normalization, linking) that transform raw text into exploitable structured entities.

📖
istilah

Knowledge-Based QA System

Type of question-answering system that finds answers by querying a structured knowledge base, populated by entity and relation extraction.

📖
istilah

Hybrid NER

Approach combining rule-based methods (pattern matching) and machine learning models to leverage the precision of the former and the flexibility of the latter.

📖
istilah

Entity Disambiguation

Task of resolving ambiguity when the same character string can refer to multiple distinct entities (e.g., 'Paris' the city vs. 'Paris' the myth).

📖
istilah

Fine-Tuning for NER

Process of adapting a pre-trained language model (like BERT) on a specific corpus for a named entity recognition task.

🔍

Tidak ada hasil ditemukan