How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Knwl.js is a JavaScript named entity recognition library and rule-based text parser. It serves as an extensible information extraction tool designed to identify and pull structured entities, such as dates, times, and locations, from unstructured text strings.
The main features of loadfive/knwl.js are: Rule-Based Text Parsers, Entity and Relation Extraction, Named Entity Recognition, Natural Language Processing, Information Extraction, Unstructured Text Processing, Core Capability Extensions, Parsing Pipeline Plugins.
Open-source alternatives to loadfive/knwl.js include: facebook/duckling — Duckling is a deterministic named entity recognizer and natural language entity extractor. It transforms unstructured… spencermountain/compromise — Compromise is a natural language processing library and rule-based text parser designed to analyze unstructured text.… stanfordnlp/stanza — Stanza is a Python natural language processing library designed for tokenization, lemmatization, and dependency… stanfordnlp/corenlp — CoreNLP is a Java natural language processing library designed to convert raw human language text into structured… ownthink/knowledgegraphdata — KnowledgeGraphData is a collection of structured datasets and corpora designed to provide a foundational layer for… axa-group/nlp.js — nlp.js is a JavaScript natural language processing library and development framework used to build natural language…
Duckling is a deterministic named entity recognizer and natural language entity extractor. It transforms unstructured text into machine-readable data by mapping language-specific input strings to universal structured formats. The system utilizes a rule-based engine and composable language rules to resolve entities without relying on probabilistic models. It supports multilingual text parsing across various regional locales, employing a logic-driven approach to normalize diverse natural language expressions into standardized numeric values. The project covers the extraction and normalization
Compromise is a natural language processing library and rule-based text parser designed to analyze unstructured text. It functions as a toolkit for identifying parts of speech, linguistic patterns, and semantic meaning, while providing specialized engines for named entity recognition and the parsing of temporal and numeric data. The project is distinguished by its linguistic morphological engine, which can conjugate verbs across different tenses and inflect nouns and adjectives. It further allows for linguistic model customization through a plugin system that enables the extension of lexicons
CoreNLP is a Java natural language processing library designed to convert raw human language text into structured data. It utilizes a suite of linguistic annotators to analyze text through a pipeline, extracting grammatical structures, sentiment, and linguistic patterns. The project includes a coreference resolution engine that links multiple mentions of the same entity to maintain contextual consistency across documents. It also provides tools for named entity recognition to categorize people, companies, and locations, and a part-of-speech tagger to assign grammatical categories and base for
Stanza is a Python natural language processing library designed for tokenization, lemmatization, and dependency parsing across many human languages using neural models. It provides a neural processing pipeline that converts raw text into structured linguistic data objects, alongside a specialized analyzer for extracting medical insights from clinical and biomedical language. The project includes a wrapper that connects Python scripts to Java-based natural language processing tools and remote annotation servers. This enables a bridge for extracting linguistic annotations and analysis data from