Analyse japanese ebooks using MeCab to determine the difficulty level for japanese learners

Overview

japanese-ebook-analysis

This aim of this project is to make analysing the contents of a japanese ebook easy and streamline the process for non-technical users. You can analyse an ebook, and see the following information:

  • The length of the book in words
  • The length of the book in characters
  • The number of unique words used in the book
  • The number of unique words that are only used once in the book
  • The percentage of unique words that are only used once
  • The number of unique characters used
  • The number of unique characters that are only used once
  • The percentage of unique characters that are only used once
  • A list of all the words used in the book as well as how often they are used
  • A list of all the characters used in the book as well as how often they are used

For text processing, we use MeCab

Usage

Currently, the project is not deployed anywhere, so to use the service, you will need to follow the steps below in the development section to get the server running.

  1. Upload a .epub file containing japanese text to the server
  2. The server will redirect you to a page showing you information about the ebook. You can then also click the 'See more details' button to see all the generated data, including a list of all the words used together with how many occurences there are for each word, and the same for the characters as well.

Development

  1. Clone repository: git clone https://github.com/christofferaakre/japanese-ebook-analysis.git
  2. Make sure you have mecab set up on your system. See http://www.robfahey.co.uk/blog/japanese-text-analysis-in-python/
    (Only required if you will actually upload ebooks or run the analyse_epub.py script), which you will not need to do to contribute to other parts of the app. for a good guide on how to set it up.
  3. Install python dependencies: pip install -r requirements.txt
  4. Install other dependencies (these all need to be in your system path):
    • pandoc
  5. Run ./app.py to start the flask dev server

Contributing

I'm very happy for any happy contributions! Before contributing, please have a look at CONTRIBUTING.md.

To see what needs work on, have a look at the repo's Issues and its Pull requests.

Feel free to submit your own issue or pull request about a new feature or anything else. When submitting a pull request, don't be afraid to modify any of the files; I'm not very attached to the coding style used in the repo.

Owner
Christoffer Aakre
Christoffer Aakre
BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network)

BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network) BERTAC is a framework that combines a

6 Jan 24, 2022
Host your own GPT-3 Discord bot

GPT3 Discord Bot Host your own GPT-3 Discord bot i'd host and make the bot invitable myself, however GPT3 terms of service prohibit public use of GPT3

[something hillarious here] 8 Jan 07, 2023
Use fastai-v2 with HuggingFace's pretrained transformers

FastHugs Use fastai v2 with HuggingFace's pretrained transformers, see the notebooks below depending on your task: Text classification: fasthugs_seq_c

Morgan McGuire 111 Nov 16, 2022
ACL'2021: Learning Dense Representations of Phrases at Scale

DensePhrases DensePhrases is an extractive phrase search tool based on your natural language inputs. From 5 million Wikipedia articles, it can search

Princeton Natural Language Processing 540 Dec 30, 2022
This Project is based on NLTK It generates a RANDOM WORD from a predefined list of words, From that random word it read out the word, its meaning with parts of speech , its antonyms, its synonyms

This Project is based on NLTK(Natural Language Toolkit) It generates a RANDOM WORD from a predefined list of words, From that random word it read out the word, its meaning with parts of speech , its

SaiVenkatDhulipudi 2 Nov 17, 2021
An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)

VizSeq is a Python toolkit for visual analysis on text generation tasks like machine translation, summarization, image captioning, speech translation

Facebook Research 409 Oct 28, 2022
C.J. Hutto 3.8k Dec 30, 2022
Code for paper: An Effective, Robust and Fairness-awareHate Speech Detection Framework

BiQQLSTM_HS Code and data for paper: Title: An Effective, Robust and Fairness-awareHate Speech Detection Framework. Authors: Guanyi Mou and Kyumin Lee

Guanyi Mou 2 Dec 27, 2022
构建一个多源(公众号、RSS)、干净、个性化的阅读环境

2C 构建一个多源(公众号、RSS)、干净、个性化的阅读环境 作为一名微信公众号的重度用户,公众号一直被我设为汲取知识的地方。随着使用程度的增加,相信大家或多或少会有一个比较头疼的问题——广告问题。 假设你关注的公众号有十来个,若一个公众号两周接一次广告,理论上你会面临二十多次广告,实际上会更多,运

howie.hu 678 Dec 28, 2022
A versatile token stream for handwritten parsers.

Writing recursive-descent parsers by hand can be quite elegant but it's often a bit more verbose than expected, especially when it comes to handling indentation and reporting proper syntax errors. Th

Valentin Berlier 8 Nov 30, 2022
An official repository for tutorials of Probabilistic Modelling and Reasoning (2021/2022) - a University of Edinburgh master's course.

PMR computer tutorials on HMMs (2021-2022) This is a repository for computer tutorials of Probabilistic Modelling and Reasoning (2021/2022) - a Univer

Vaidotas Šimkus 10 Dec 06, 2022
Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated

Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated. This engine can later be used for downstream tasks in NLP such as Q&A, summarization, generation

Diego 1 Mar 20, 2022
News-Articles-and-Essays - NLP (Topic Modeling and Clustering)

NLP T5 Project proposal Topic Modeling and Clustering of News-Articles-and-Essays Students: Nasser Alshehri Abdullah Bushnag Abdulrhman Alqurashi OVER

2 Jan 18, 2022
Official code for Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Official code for our Interspeech 2021 - Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset [1]*. Visually-grounded spoken language datasets c

Ian Palmer 3 Jan 26, 2022
Speech Recognition for Uyghur using Speech transformer

Speech Recognition for Uyghur using Speech transformer Training: this model using CTC loss and Cross Entropy loss for training. Download pretrained mo

Uyghur 11 Nov 17, 2022
Optimal Transport Tools (OTT), A toolbox for all things Wasserstein.

Optimal Transport Tools (OTT), A toolbox for all things Wasserstein. See full documentation for detailed info on the toolbox. The goal of OTT is to pr

OTT-JAX 255 Dec 26, 2022
Tutorial to pretrain & fine-tune a 🤗 Flax T5 model on a TPUv3-8 with GCP

Pretrain and Fine-tune a T5 model with Flax on GCP This tutorial details how pretrain and fine-tune a FlaxT5 model from HuggingFace using a TPU VM ava

Gabriele Sarti 41 Nov 18, 2022
Guide to using pre-trained large language models of source code

Large Models of Source Code I occasionally train and publicly release large neural language models on programs, including PolyCoder. Here, I describe

Vincent Hellendoorn 947 Dec 28, 2022
TLA - Twitter Linguistic Analysis

TLA - Twitter Linguistic Analysis Tool for linguistic analysis of communities TLA is built using PyTorch, Transformers and several other State-of-the-

Tushar Sarkar 47 Aug 14, 2022
Sequence Modeling with Structured State Spaces

Structured State Spaces for Sequence Modeling This repository provides implementations and experiments for the following papers. S4 Efficiently Modeli

HazyResearch 902 Jan 06, 2023