Ner-Lstm

Visit Tool

ner-lstm is an open-source Coding & Development tool that implements Named Entity Recognition using multilayered bidirectional LSTMs. It is built with TensorFlow and supports both English and Hindi languages.

Claim this tool

1View

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ner-lstm?

ner-lstm is an open-source project that provides an implementation of Named Entity Recognition (NER) using multilayered bidirectional Long Short-Term Memory (LSTM) networks. This tool is based on the approach described in a research paper published at the ICON-16 conference. It leverages TensorFlow for its deep learning architecture and supports classification tasks for named entities in text corpora. The project includes functionalities for generating embedding models (Word2Vec, GloVe, RnnVec), preparing input data by resizing datasets and converting sentences to embeddings, and running the deep neural network. It has been tested on CoNNL 2003 NER Shared Task and the ICON-2013 Hindi NER dataset, demonstrating its applicability to both English and Hindi languages. The code is available on GitHub, making it accessible for developers and researchers interested in natural language processing.

Best used for

Ideal for developers and professors who need to implement Named Entity Recognition, train deep learning models for text classification, and experiment with different word embeddings. Especially valuable for researchers working with both English and Hindi language datasets in NLP.

Common actions

perform named entity recognition

train deep learning models

process natural language

experiment with LSTMs

github copilot"AI Agents"face swappingopen-sourceautomated workflowdeepfakelow-code/no-codeworkflowscollaboration

Capabilities

Key features

Multilayered bidirectional LSTM
TensorFlow implementation
Word2Vec/GloVe/RnnVec embeddings
Input data preparation
F1 score calculation
Save/restore models

Target Audience

developerprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning architecture does ner-lstm use for Named Entity Recognition?

ner-lstm utilizes a 2-layer bidirectional LSTM (Long Short-Term Memory) network, implemented using TensorFlow. This architecture is designed to classify named entities within text corpora, building upon the approach detailed in a research paper published at the ICON-16 conference.

What types of word embeddings can be used with ner-lstm?

The tool supports generating and using several types of word embeddings, including Word2Vec, GloVe, and RnnVec. Users can either train new models from a corpus or load existing ones, providing flexibility in how words are represented for the neural network.

Does ner-lstm support languages other than English?

Yes, ner-lstm has been applied to the NER Hindi dataset from ICON-2013 proceedings. It includes utilities like `hindi_util.py` to convert Hindi corpora to an English-compatible format, enabling its use for Named Entity Recognition in Hindi.

Trending

Subcategories trending in Coding & Development

Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

Trending

Also listed in

This tool also appears in

Research & Education › Academic Research

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce