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
Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks for Sentence Classification Code for the paper Convolutional Neural Networks for Sentence Classification (EMNLP 2014). R

Yoon Kim 2k Jan 02, 2023
Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP)

Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (

jawahar 20 Apr 30, 2022
A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.

A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.

NEC Laboratories Europe 13 Sep 08, 2022
This repository contains the code for "Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference"

Pattern-Exploiting Training (PET) This repository contains the code for Exploiting Cloze Questions for Few-Shot Text Classification and Natural Langua

Timo Schick 1.4k Dec 30, 2022
Graphical user interface for Argos Translate

Argos Translate GUI Website | GitHub | PyPI Graphical user interface for Argos Translate. Install pip3 install argostranslategui

Argos Open Tech 16 Dec 07, 2022
Module for automatic summarization of text documents and HTML pages.

Automatic text summarizer Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains sim

Mišo Belica 3k Jan 08, 2023
Multispeaker & Emotional TTS based on Tacotron 2 and Waveglow

This Repository contains a sample code for Tacotron 2, WaveGlow with multi-speaker, emotion embeddings together with a script for data preprocessing.

Ivan Didur 106 Jan 01, 2023
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recogniti

Soohwan Kim 26 Dec 14, 2022
Fast, general, and tested differentiable structured prediction in PyTorch

Torch-Struct: Structured Prediction Library A library of tested, GPU implementations of core structured prediction algorithms for deep learning applic

HNLP 1.1k Dec 16, 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
Code for EMNLP20 paper: "ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training"

ProphetNet-X This repo provides the code for reproducing the experiments in ProphetNet. In the paper, we propose a new pre-trained language model call

Microsoft 394 Dec 17, 2022
Command Line Text-To-Speech using Google TTS

cli-tts Thanks to gTTS by @pndurette! This is an interactive command line text-to-speech tool using Google TTS. Just type text and the voice will be p

ReekyStive 3 Nov 11, 2022
Beyond Accuracy: Behavioral Testing of NLP models with CheckList

CheckList This repository contains code for testing NLP Models as described in the following paper: Beyond Accuracy: Behavioral Testing of NLP models

Marco Tulio Correia Ribeiro 1.8k Dec 28, 2022
SIGIR'22 paper: Axiomatically Regularized Pre-training for Ad hoc Search

Introduction This codebase contains source-code of the Python-based implementation (ARES) of our SIGIR 2022 paper. Chen, Jia, et al. "Axiomatically Re

Jia Chen 17 Nov 09, 2022
Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset.

Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset.

18 Nov 28, 2022
MMDA - multimodal document analysis

MMDA - multimodal document analysis

AI2 75 Jan 04, 2023
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

GPT2-Pytorch with Text-Generator Better Language Models and Their Implications Our model, called GPT-2 (a successor to GPT), was trained simply to pre

Tae-Hwan Jung 775 Jan 08, 2023
Script to download some free japanese lessons in portuguse from NHK

Nihongo_nhk This is a script to download some free japanese lessons in portuguese from NHK. It can be executed by installing the packages with: pip in

Matheus Alves 2 Jan 06, 2022
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Phil Wang 5k Jan 02, 2023
A PyTorch-based model pruning toolkit for pre-trained language models

English | 中文说明 TextPruner是一个为预训练语言模型设计的模型裁剪工具包,通过轻量、快速的裁剪方法对模型进行结构化剪枝,从而实现压缩模型体积、提升模型速度。 其他相关资源: 知识蒸馏工具TextBrewer:https://github.com/airaria/TextBrewe

Ziqing Yang 231 Jan 08, 2023