Gold standard corpus annotated with verb-preverb connections for Hungarian.

Overview

Hungarian Preverb Corpus

A gold standard corpus manually annotated with verb-preverb connections for Hungarian.

corpus

The corpus consist of the following 4 files:

filename # sentences # preverbs
difficult_validate1.txt 310 357
difficult_validate2.txt 840 935
difficult_test.txt 327 376
general_test.txt 503 500

Preverbs in the general dataset are in the distribution as they appear in normal Hungarian text. The difficult dataset is specially crafted: the most common and most-easy-to-handle pattern, i.e. when a verb is directly followed by its preverb (e.g. megy ki 'go out'), is omitted. validate is for development/validation, test is for testing. Note that a general_validate dataset would not be useful, because the trivial pattern would be in vast majority overwhelming the more interesting less frequent patterns.

Accordingly, the emPreverb tool which connects preverbs to their corresponding verb, was developed based only on interesting difficult examples, and tested both on difficult and general data.

(Remark. The difficult_validate dataset is divided into two parts for historical reasons, but you can simply use them together: they consist a total of 1150 sentences and 1292 preverbs.)

corpus annotation guidelines

  • Preverb marked by a suffixed backslash followed by a (single digit!) ID number: meg\1.
  • Word from which the preverb was separated marked by a pipe followed by the same ID number: főzve|1.
  • Within the same line, different verb-prefix pairs must (obviously) receive different ID numbers.
  • A preverb that does not belong to any word in the sentence (ellipsis etc.) is marked with a zero ID: "Hazakísérhetlek?" "Meg\0 hát." Any number of preverbs can have the 0 ID within the same line.
  • In the difficult dataset, a verb directly followed by its preverb is not annotated: főzte meg, but: főzte|1 volna meg\1.
  • In the general dataset, the first pattern is annotated as well: főzte|1 meg\1.
  • Normally there is a 1:1 correspondence between preverbs and verbs. However, there are exceptions, and these are annotated accordingly, e.g. Se ki\1, se be\1 nem lehetett menni|1 Budakesziről; át-\1 meg átjárták|1.

Check (see Step 1 to 4 in evaluate.ipynb) whether tokens annotated as separated preverbs are also analysed by e-magyar morph,pos as preverbs. If not (e.g. if the preverb meg is tagged by emtsv as a [/Conj]), remove this annotation (or the whole item if no annotation left) from the dataset because preverb will necessarily fail due to incorrect emtsv annotation, which is extraneous to its performance evaluation. Exception: person-inflected preverb-like postpositions such as in utánam\1 dobják|1, which are tagged by emtsv as [/Post], and case-inflected personal pronouns such as in hozzá\1 voltam szokva|1, which are tagged as [/N|Pro], should not be removed from the dataset since preverb should be able to handle these.

If a token is annotated as the verb stem counterpart of a separated preverb, but is not tagged by emtsv as a verb, check whether the preverb annotation is correct, but if so, do not remove this annotation from the dataset. preverb is supposed to be able to handle the connection of such separated preverbs.

evaluation

An environment for reproducing evaluation of emPreverb as published in the paper below.

git clone https://github.com/ril-lexknowrep/emPreverb
cd emPreverb
make evaluate

Note that make evaluate clones this current repo inside emPreverb and runs evaluation.

The results are obtained in general_test_results.txt and difficult_test_results.txt. This should be exactly the same which can be found in Table 3 of the paper below.

development

An environment used for developing emPreverb. It is "for us" but if you insist to use it:

git clone https://github.com/ril-lexknowrep/emPreverb
cd emPreverb
git clone https://github.com/ril-lexknowrep/hungarian-preverb-corpus
cd hungarian-preverb-corpus/development
jupyter notebook evaluate.ipynb

(Remark. Yes, please clone this repo inside emPreverb.)

citation

If you use the corpus, please cite the following paper.

Pethő, Gergely and Sass, Bálint and Kalivoda, Ágnes and Simon, László and Lipp, Veronika: Igekötő-kapcsolás. In: MSZNY 2022.

Owner
RIL Lexical Knowledge Representation Research Group
RIL Lexical Knowledge Representation Research Group
Pipeline for training LSA models using Scikit-Learn.

Latent Semantic Analysis Pipeline for training LSA models using Scikit-Learn. Usage Instead of writing custom code for latent semantic analysis, you j

Dani El-Ayyass 23 Sep 05, 2022
Develop open-source Python Arabic NLP libraries that the Arab world will easily use in all Natural Language Processing applications

Develop open-source Python Arabic NLP libraries that the Arab world will easily use in all Natural Language Processing applications

BADER ALABDAN 2 Oct 22, 2022
Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

PLBART Code pre-release of our work, Unified Pre-training for Program Understanding and Generation accepted at NAACL 2021. Note. A detailed documentat

Wasi Ahmad 138 Dec 30, 2022
A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex)

CodeJ A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex) Install requirements pip install -r

TheProtagonist 1 Dec 06, 2021
precise iris segmentation

PI-DECODER Introduction PI-DECODER, a decoder structure designed for Precise Iris Segmentation and Location. The decoder structure is shown below: Ple

8 Aug 08, 2022
This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Technique for Text Classification

The baseline code is for EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks

Akbar Karimi 81 Dec 09, 2022
Natural Language Processing Specialization

Natural Language Processing Specialization In this folder, Natural Language Processing Specialization projects and notes can be found. WHAT I LEARNED

Kaan BOKE 3 Oct 06, 2022
Chinese named entity recognization (bert/roberta/macbert/bert_wwm with Keras)

Chinese named entity recognization (bert/roberta/macbert/bert_wwm with Keras)

2 Jul 05, 2022
Yes it's true :broken_heart:

Information WARNING: No longer hosted If you would like to be on this repo's readme simply fork or star it! Forks 1 - Flowzii 2 - Errorcrafter 3 - vk-

Dropout 66 Dec 31, 2022
Smart discord chatbot integrated with Dialogflow to manage different classrooms and assist in teaching!

smart-school-chatbot Smart discord chatbot integrated with Dialogflow to interact with students naturally and manage different classes in a school. De

Tom Huynh 5 Oct 24, 2022
Official code repository of the paper Linear Transformers Are Secretly Fast Weight Programmers.

Linear Transformers Are Secretly Fast Weight Programmers This repository contains the code accompanying the paper Linear Transformers Are Secretly Fas

Imanol Schlag 77 Dec 19, 2022
Chinese NER with albert/electra or other bert descendable model (keras)

Chinese NLP (albert/electra with Keras) Named Entity Recognization Project Structure ./ ├── NER │   ├── __init__.py │   ├── log

2 Nov 20, 2022
A telegram bot to translate 100+ Languages

🔥 GOOGLE TRANSLATER 🔥 The owner would not be responsible for any kind of bans due to the bot. • ⚡ INSTALLING ⚡ • • 🔰 Deploy To Railway 🔰 • • ✅ OFF

Aɴᴋɪᴛ Kᴜᴍᴀʀ 5 Dec 20, 2021
A fast, efficient universal vector embedding utility package.

Magnitude: a fast, simple vector embedding utility library A feature-packed Python package and vector storage file format for utilizing vector embeddi

Plasticity 1.5k Jan 02, 2023
AI-Broad-casting - AI Broad casting with python

Basic Code 1. Use The Code Configuration Environment conda create -n code_base p

Code for our paper "Transfer Learning for Sequence Generation: from Single-source to Multi-source" in ACL 2021.

TRICE: a task-agnostic transferring framework for multi-source sequence generation This is the source code of our work Transfer Learning for Sequence

THUNLP-MT 9 Jun 27, 2022
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
TextFlint is a multilingual robustness evaluation platform for natural language processing tasks,

TextFlint is a multilingual robustness evaluation platform for natural language processing tasks, which unifies general text transformation, task-specific transformation, adversarial attack, sub-popu

TextFlint 587 Dec 20, 2022
Korea Spell Checker

한국어 문서 koSpellPy Korean Spell checker How to use Install pip install kospellpy Use from kospellpy import spell_init spell_checker = spell_init() # d

kangsukmin 2 Oct 20, 2021
Graph4nlp is the library for the easy use of Graph Neural Networks for NLP

Graph4NLP Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP).

Graph4AI 1.5k Dec 23, 2022