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
Generating Korean Slogans with phonetic and structural repetition

LexPOS_ko Generating Korean Slogans with phonetic and structural repetition Generating Slogans with Linguistic Features LexPOS is a sequence-to-sequen

Yeoun Yi 3 May 23, 2022
Code for ACL 2022 main conference paper "STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation".

STEMM: Self-learning with Speech-Text Manifold Mixup for Speech Translation This is a PyTorch implementation for the ACL 2022 main conference paper ST

ICTNLP 29 Oct 16, 2022
硕士期间自学的NLP子任务,供学习参考

NLP_Chinese_down_stream_task 自学的NLP子任务,供学习参考 任务1 :短文本分类 (1).数据集:THUCNews中文文本数据集(10分类) (2).模型:BERT+FC/LSTM,Pytorch实现 (3).使用方法: 预训练模型使用的是中文BERT-WWM, 下载地

12 May 31, 2022
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5

NLP-Summarizer Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5 This project aimed to provide in

Samuel Sharkey 1 Feb 07, 2022
Задания КЕГЭ по информатике 2021 на Python

КЕГЭ 2021 на Python В этом репозитории мои решения типовых заданий КЕГЭ по информатике в 2021 году, БЕСПЛАТНО! Задания Взяты с https://inf-ege.sdamgia

8 Oct 13, 2022
CCF BDCI BERT系统调优赛题baseline(Pytorch版本)

CCF BDCI BERT系统调优赛题baseline(Pytorch版本) 此版本基于Pytorch后端的huggingface进行实现。由于此实现使用了Oneflow的dataloader作为数据读入的方式,因此也需要安装Oneflow。其它框架的数据读取可以参考OneflowDataloade

Ziqi Zhou 9 Oct 13, 2022
Kerberoast with ACL abuse capabilities

targetedKerberoast targetedKerberoast is a Python script that can, like many others (e.g. GetUserSPNs.py), print "kerberoast" hashes for user accounts

Shutdown 213 Dec 22, 2022
Use the state-of-the-art m2m100 to translate large data on CPU/GPU/TPU. Super Easy!

Easy-Translate is a script for translating large text files in your machine using the M2M100 models from Facebook/Meta AI. We also privide a script fo

Iker García-Ferrero 41 Dec 15, 2022
PyTorch Implementation of "Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging" (Findings of ACL 2022)

Feature_CRF_AE Feature_CRF_AE provides a implementation of Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging

Jacob Zhou 6 Apr 29, 2022
端到端的长本文摘要模型(法研杯2020司法摘要赛道)

端到端的长文本摘要模型(法研杯2020司法摘要赛道)

苏剑林(Jianlin Su) 334 Jan 08, 2023
1 Jun 28, 2022
Semantic search for quotes.

squote A semantic search engine that takes some input text and returns some (questionably) relevant (questionably) famous quotes. Built with: bert-as-

cjwallace 11 Jun 25, 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
189 Jan 02, 2023
nlabel is a library for generating, storing and retrieving tagging information and embedding vectors from various nlp libraries through a unified interface.

nlabel is a library for generating, storing and retrieving tagging information and embedding vectors from various nlp libraries through a unified interface.

Bernhard Liebl 2 Jun 10, 2022
Code associated with the Don't Stop Pretraining ACL 2020 paper

dont-stop-pretraining Code associated with the Don't Stop Pretraining ACL 2020 paper Citation @inproceedings{dontstoppretraining2020, author = {Suchi

AI2 449 Jan 04, 2023
HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools

HuggingSound HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools. I have no intention of building a very complex tool here.

Jonatas Grosman 247 Dec 26, 2022
MHtyper is an end-to-end pipeline for recognized the Forensic microhaplotypes in Nanopore sequencing data.

MHtyper is an end-to-end pipeline for recognized the Forensic microhaplotypes in Nanopore sequencing data. It is implemented using Python.

willow 6 Jun 27, 2022
CYGNUS, the Cynical AI, combines snarky responses with uncanny aggression.

New & (hopefully) Improved CYGNUS with several API updates, user updates, and online/offline operations added!!!

Simran Farrukh 0 Mar 28, 2022
A CSRankings-like index for speech researchers

Speech Rankings This project mimics CSRankings to generate an ordered list of researchers in speech/spoken language processing along with their possib

Mutian He 19 Nov 26, 2022