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
Easy to start. Use deep nerual network to predict the sentiment of movie review.

Easy to start. Use deep nerual network to predict the sentiment of movie review. Various methods, word2vec, tf-idf and df to generate text vectors. Various models including lstm and cov1d. Achieve f1

1 Nov 19, 2021
A simple visual front end to the Maya UE4 RBF plugin delivered with MetaHumans

poseWrangler Overview PoseWrangler is a simple UI to create and edit pose-driven relationships in Maya using the MayaUE4RBF plugin. This plugin is dis

Christopher Evans 105 Dec 18, 2022
In this project, we aim to achieve the task of predicting emojis from tweets. We aim to investigate the relationship between words and emojis.

Making Emojis More Predictable by Karan Abrol, Karanjot Singh and Pritish Wadhwa, Natural Language Processing (CSE546) under the guidance of Dr. Shad

Karanjot Singh 2 Jan 17, 2022
NLP tool to extract emotional phrase from tweets 🤩

Emotional phrase extractor Extract phrase in the given text that is used to express the sentiment. Capturing sentiment in language is important in the

Shahul ES 38 Oct 17, 2022
Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

KoSimCSE Korean Simple Contrastive Learning of Sentence Embeddings implementation using pytorch SimCSE Installation git clone https://github.com/BM-K/

34 Nov 24, 2022
Source code for the paper "TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations"

TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations Created by Jiahao Pang, Duanshun Li, and Dong Tian from InterDigital In

InterDigital 21 Dec 29, 2022
Galois is an auto code completer for code editors (or any text editor) based on OpenAI GPT-2.

Galois is an auto code completer for code editors (or any text editor) based on OpenAI GPT-2. It is trained (finetuned) on a curated list of approximately 45K Python (~470MB) files gathered from the

Galois Autocompleter 91 Sep 23, 2022
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
In this repository we have tested 3 VQA models on the ImageCLEF-2019 dataset.

Med-VQA In this repository we have tested 3 VQA models on the ImageCLEF-2019 dataset. Two of these are made on top of Facebook AI Reasearch's Multi-Mo

Kshitij Ambilduke 8 Apr 14, 2022
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early

LEE YOON HYUNG 147 Dec 05, 2022
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Dense Passage Retrieval Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the

Meta Research 1.1k Jan 07, 2023
Revisiting Pre-trained Models for Chinese Natural Language Processing (Findings of EMNLP 2020)

This repository contains the resources in our paper "Revisiting Pre-trained Models for Chinese Natural Language Processing", which will be published i

Yiming Cui 463 Dec 30, 2022
A program that uses real statistics to choose the best times to bet on BloxFlip's crash gamemode

Bloxflip Smart Bet A program that uses real statistics to choose the best times to bet on BloxFlip's crash gamemode. https://bloxflip.com/crash. THIS

43 Jan 05, 2023
NL. The natural language programming language.

NL A Natural-Language programming language. Built using Codex. A few examples are inside the nl_projects directory. How it works Write any code in pur

2 Jan 17, 2022
Simple telegram bot to convert files into direct download link.you can use telegram as a file server 🪁

TGCLOUD 🪁 Simple telegram bot to convert files into direct download link.you can use telegram as a file server 🪁 Features Easy to Deploy Heroku Supp

Mr.Acid dev 6 Oct 18, 2022
Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

Semantic search through Wikipedia with the Weaviate vector search engine Weaviate is an open source vector search engine with build-in vectorization a

SeMI Technologies 191 Dec 26, 2022
This is the offline-training-pipeline for our project.

offline-training-pipeline This is the offline-training-pipeline for our project. We adopt the offline training and online prediction Machine Learning

0 Apr 22, 2022
skweak: A software toolkit for weak supervision applied to NLP tasks

Labelled data remains a scarce resource in many practical NLP scenarios. This is especially the case when working with resource-poor languages (or text domains), or when using task-specific labels wi

Norsk Regnesentral (Norwegian Computing Center) 850 Dec 28, 2022
Traditional Chinese Text Recognition Dataset: Synthetic Dataset and Labeled Data

Traditional Chinese Text Recognition Dataset: Synthetic Dataset and Labeled Data Authors: Yi-Chang Chen, Yu-Chuan Chang, Yen-Cheng Chang and Yi-Ren Ye

Yi-Chang Chen 5 Dec 15, 2022