atis-dataset

There are 14 repositories under atis-dataset topic.

  • yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification

    This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.

    Language:Python463101596

  • sz128/slot_filling_and_intent_detection_of_SLU

    slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet

    Language:Python389189104

  • howl-anderson/ATIS_dataset

    The ATIS (Airline Travel Information System) Dataset

    Language:Python1525148

  • howl-anderson/NLU_benchmark_dataset

    自然语言理解 基准测试 数据集 | Benchmark datasets for Natural Language Understanding (NLU)

    Language:Python212111

  • Se-Hun/NLU-Benchmark

    Natural Language Understanding(NLU) benchmark for closed domain chatbot and task-oriented artificial intelligence secretary system

    Language:Python4200

  • Toukenize/weather-flight-bot

    Weather-Flight-Bot, a combined model of semantics slot-filling and intent detection powered by neural network (LSTM and CNN layers).

    Language:Jupyter Notebook4001

  • davidemodolo/NLU_Intent_and_Slot

    Intent detection and Slot filling joint learning on ATIS and SNIPS datasets using pre-trained models and built-from-scratch models

    Language:Jupyter Notebook3101

  • fedelopez77/seqtag

    Named Entity Recognition on ATIS dataset with Transformers

    Language:Python1100

  • Jules010209/bordeaux-atis

    Language:TypeScript1100

  • nawaz-kmr/Airline-Travel-Information-System-ATIS-Text-Analysis

    In this project, you will learn how to generate a complete semantic parse of utterances. First, you will make a discovery on your dataset to get insights about the dataset analytics. Then, you will learn, to extract entities with two different techniques – with spaCy Matcher and by walking on the dependency tree. Next, you will learn different ways of performing intent recognition by analyzing the sentence structure. Finally, you will put all the information together to generate a semantic parse.

    Language:Jupyter Notebook1100

  • egillanton/ice-atis

    The Icelandic translation of the ATIS dataset

    Language:Python0200

  • screddy1313/cky-parser

    CKY parser for ATIS grammar

    Language:Python0200

  • hamin123/Intent-Classification

    Intent Classification Example Using ATIS dataset

    Language:Jupyter Notebook10

  • Ipsedo/ProjetTAL

    Projet de Traitement Automatique des Langues sur les données ATIS

    Language:Python20