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
gaiic2021-track3-小布助手对话短文本语义匹配复赛rank3、决赛rank4

决赛答辩已经过去一段时间了,我们队伍ac milan最终获得了复赛第3,决赛第4的成绩。在此首先感谢一些队友的carry~ 经过2个多月的比赛,学习收获了很多,也认识了很多大佬,在这里记录一下自己的参赛体验和学习收获。

102 Dec 19, 2022
Framework for fine-tuning pretrained transformers for Named-Entity Recognition (NER) tasks

NERDA Not only is NERDA a mesmerizing muppet-like character. NERDA is also a python package, that offers a slick easy-to-use interface for fine-tuning

Ekstra Bladet 141 Dec 30, 2022
The swas programming language

The Swas programming language This is a language that was made for fun. Installation Step 0: Make sure you have python installed Step 1. Clone this re

Swas.py 19 Jul 18, 2022
Write Python in Urdu - اردو میں کوڈ لکھیں

UrduPython Write simple Python in Urdu. How to Use Write Urdu code in سامپل۔پے The mappings are as following: "۔": ".", "،":

Saad A. Bazaz 26 Nov 27, 2022
Python library for Serbian Natural language processing (NLP)

SrbAI - Python biblioteka za procesiranje srpskog jezika SrbAI je projekat prikupljanja algoritama i modela za procesiranje srpskog jezika u jedinstve

Serbian AI Society 3 Nov 22, 2022
Simple Python script to scrape youtube channles of "Parity Technologies and Web3 Foundation" and translate them to well-known braille language or any language

Simple Python script to scrape youtube channles of "Parity Technologies and Web3 Foundation" and translate them to well-known braille language or any

Little Endian 1 Apr 28, 2022
Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Spanish Language Models 💃🏻 Corpora 📃 Corpora Number of documents Size (GB) BNE 201,080,084 570GB Models 🤖 RoBERTa-base BNE: https://huggingface.co

PlanTL-SANIDAD 203 Dec 20, 2022
A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.

MONEYBALL - ChatBot Module: 4006CEM, Class: B, Group: 5 Contributors: Jonas Djondo Roshan Kc Cole Samson Daniel Rodrigues Ihteshaam Naseer Kind remind

Jonas Djondo 1 Nov 18, 2021
This repository serves as a place to document a toy attempt on how to create a generative text model in Catalan, based on GPT-2

GPT-2 Catalan playground and scripts to train a GPT-2 model either from scrath or from another pretrained model.

Laura 1 Jan 28, 2022
Enterprise Scale NLP with Hugging Face & SageMaker Workshop series

Workshop: Enterprise-Scale NLP with Hugging Face & Amazon SageMaker Earlier this year we announced a strategic collaboration with Amazon to make it ea

Philipp Schmid 161 Dec 16, 2022
Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles

Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles (TASLP 2022)

Zhuosheng Zhang 3 Apr 14, 2022
MRC approach for Aspect-based Sentiment Analysis (ABSA)

B-MRC MRC approach for Aspect-based Sentiment Analysis (ABSA) Paper: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extracti

Phuc Phan 1 Apr 05, 2022
Translators - is a library which aims to bring free, multiple, enjoyable translation to individuals and students in Python

Translators - is a library which aims to bring free, multiple, enjoyable translation to individuals and students in Python

UlionTse 907 Dec 27, 2022
An extension for asreview implements a version of the tf-idf feature extractor that saves the matrix and the vocabulary.

Extension - matrix and vocabulary extractor for TF-IDF and Doc2Vec An extension for ASReview that adds a tf-idf extractor that saves the matrix and th

ASReview 4 Jun 17, 2022
Plugin repository for Macast

Macast-plugins Plugin repository for Macast. How to use third-party player plugin Download Macast from GitHub Release. Download the plugin you want fr

109 Jan 04, 2023
jiant is an NLP toolkit

jiant is an NLP toolkit The multitask and transfer learning toolkit for natural language processing research Why should I use jiant? jiant supports mu

ML² AT CILVR 1.5k Jan 04, 2023
A Plover python dictionary allowing for consistent symbol input with specification of attachment and capitalisation in one stroke.

Emily's Symbol Dictionary Design This dictionary was created with the following goals in mind: Have a consistent method to type (pretty much) every sy

Emily 68 Jan 07, 2023
Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch

N-Grammer - Pytorch Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch Install $ pip install n-grammer-pytorch Usage

Phil Wang 66 Dec 29, 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod

20.5k Jan 08, 2023
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)

Time-aware Large Kernel (TaLK) Convolutions (Lioutas et al., 2020) This repository contains the source code, pre-trained models, as well as instructio

Vasileios Lioutas 28 Dec 07, 2022