#entity recognition

Wikipedia2Vec is a versatile tool for creating embeddings of words and entities using data from Wikipedia, developed by Studio Ousia. It enables concurrent learning of embeddings with a straightforward command line interface. Employing the skip-gram model, it positions similar entities in proximity within a vector space. Supporting 12 languages, it is effectively utilized in various tasks such as entity linking, named entity recognition, and text classification. Extensive documentation and pretrained models are available for broader applications.

Explore a fast and versatile C# Natural Language Processing library offering efficient non-destructive tokenization, flexible entity recognition, and reliable language detection. Catalyst facilitates FastText and StarSpace embeddings training, with readily available pre-trained models. Compatible with Windows, Linux, and macOS, it offers robust tools for semantic analysis. It suits projects requiring quick processing, aligning with .NET standard 2.0 for smooth pipeline integration.

HarvestText is a text mining toolkit that specializes in unsupervised text analysis and domain knowledge integration to efficiently process domain-specific texts. It supports tasks such as entity linking, sentiment analysis, and key phrase extraction. Suitable for various text preprocessing and exploratory analyses in fields like literature, web content, and more. The tool is Python 3.6+ compatible and aids in named entity recognition and dependency parsing without complex setups.

Chatbot NER by Haptik is an open-source framework designed to identify entities in text for conversational AI. It supports languages including English, Hindi, Gujarati, Marathi, Bengali, and Tamil, and processes code-mixed forms using NLP methods for extracting entities even from limited data. The API is structured for ease of use in AI applications, with the potential for adaptation to more Indian languages and dialects. Contributors can enhance its capabilities by adding training data and detection patterns.