- TDD·Last edited by Arda Göktoğan on Aug 7, 2022Linked with GitHubContributed by
There is no commentSelect some text and then click Comment, or simply add a comment to this page from below to start a discussion.140 Poem
140 Poem dataset contains 140 Turkish poems from 7 different authors, each has 20 coloumn writings. Dataset is genereated by Kemik Natural Language Processing Group.
Dataset Details
The dataset consists of 140 singly authored documents written by 7 different authors, with 20 different texts written by each author. The average length of texts is 109 words.
Samples
A sample instance is presented below.
Example:
Acının tutanakçısıyım
Acının tutanakçısıyım
Anlatıp dururum aşkları
Ayrılıkları ve o destan
Yalnızlığını ömrümüzün
Göçebe, Gezgin ve Aylak
Birmiydim aklıma gelmedi
ir çingeneyle bir bilici
Hep ayni şeydi bildiğim
Ve serseriliğimdi aşklar
Bir masalcıydım belki de
Yaşadım o büyük serüvenleri
Yolculuklar tarihimdi benim
Acılar yaşanıyordu yurdumda
Pespese yakılıyordu kentler
Bense hep oralardaydım
Daha yangın başlamadan önce
Fields
Each file presents a poem and poem belong to same author are contained in the same directory.
Splits
No split is provided by the dataset creators.
Dataset Creation
Curation Rationale
The main goal for this dataset is text classification by their authors.
Data Source
The authors gathered the poem websites.
Considerations
Social Impact of Dataset
This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures. It is also important for studeies about the non-formal representations of the language.
Dataset Curators
Published by Banu Diri and Fatih Amasyali
Citation Information
@article{diri2006identifying,
author = {Diri B., Amasyalı M. F.},
title = {Identifying the poets of the anonymous poems},
journal = {TAINN},
adress = {Canakkale}
year = {2003}
}
Read more
TRopBank: Turkish PropBank V2.0
In this paper, we present and explain TRopBank “Turkish PropBank v2.0”. PropBank is a hand-annotated corpus of propositions which is used to obtain the predicate-argument information of a language. Predicate-argument information of a language can help understand semantic roles of arguments. “Turkish PropBank v2.0”, unlike PropBank v1.0, has a much more extensive list of Turkish verbs, with 17.673 verbs in total. The original paper can be found from here and you can access the original repository TurkishPropBank. Dataset Details For TRropBank, a total of 17,691 verbs were annotated. As the data suggests, unaccusative verbs that require a patient or theme in the ARG1 column constitute roughly 15.1% of all the annotated verbs (see Table). Based on the data, it can be inferred that Turkish has an evident preference for verbs that require an ARG0 over ones that require an ARG1 as their subject. Moreover, we can see that a significant portion of Turkish verbs, 47.9% to be exact, have the transitive framework. Turkish displays an observable preference regarding transitivity. Furthermore, having predicates that do not require any arguments, Turkish diverges from the majority of the languages whose PropBanks have been reviewed in Section 2 in the paper. Even though predicates without arguments (idiomatic structures) make up less than 1% of the total, the existence of such a divergence is significant.
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Oct 16, 2022Turkish FrameNet
Introduced in 1997, FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) has been developed by the International Computer Science Institute in Berkeley, California. It is a growing computational lexicography project that offers in-depth semantic information on English words and predicates. Based on the theory of Frame Semantics by Fillmore (Fillmore and others, 1976; Fillmore, 2006), FrameNet offers semantic information on predicate-argument structure in a way that is loosely similar to wordnet (Kilgarriff and Fellbaum, 2000). In FrameNet, predicates and related lemmas are categorized under frames. The notion of frame here is thoroughly described in Frame Semantics as a schematic representation of an event, state or relationship. These semantic information packets called frames are constituted of individual lemmas (also known as Lexical Units) and frame elements (such as the agent, theme, instrument, duration, manner, direction etc.). Frame elements can be described as semantic roles that are related to the frame. Lexical Units, or lemmas, are linked to a frame through a single sense. For instance, the lemma ”roast” can mean to criticise harshly or to cook by exposing to dry heat. With its latter meaning, ”roast” belongs to the Apply Heat frame. In this version of Turkish FrameNet, we aimed to release a version of Turkish FrameNet that captures at least a considerable majority of the most frequent predicates, thus offering a valuable and practical resource from day one. Because Turkish is a low-resource language, it was important to ensure that FrameNet had enough coverage that it could be incorporated into NLP solutions as soon as it is released to the public. The original paper can be found from here and you can access the original repository TurkishFrameNet. Dataset Details In this study, a total number of 139 Frames in 8 domains were created. 16 of these frames were created specifically for Turkish while the remaining 123 are translated from English FrameNet. These frames include a total number of 2769 synsets (See Table). As we used Turkish WordNet and PropBank’s repositories, the Lexical Units were made of wordnet synsets. Thus some LUs contain more than one predicate. The total number of predicates annotated in this study is 4080. In other words, 4080 predicates were annotated into their respective frames. Sample sentences of all were marked up for the specific roles in them.
Oct 16, 2022
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