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To What Extent are Name Variants Used as Named Entities in Turkish Tweets?

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Dilek Küçük

Computation and Language · Submitted 17 Dec 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The analysis presented and the accompanying annotations will contribute to related research on the treatment of named entities in social media.

how it is stated · asserted 0.55 read the record →

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arXiv ID1912.07940 (version 1)
Pith IDBMBUZGN7, Pith's permanent record of this paper and its review
CitationDilek K\"u\c{c}\"uk. "To What Extent are Name Variants Used as Named Entities in Turkish Tweets?." arXiv:1912.07940 (2019). https://pith.science/paper/BMBUZGN7
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,897 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.08 · human participants 0.10 · controlled comparison 0.13 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.05 · mentions code or data availability 0.92.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Social media texts differ from regular texts in various aspects. One of the main differences is the common use of informal name variants instead of well-formed named entities in social media compared to regular texts. These name variants may come in the form of abbreviations, nicknames, contractions, and hypocoristic uses, in addition to names distorted due to capitalization and writing errors. In this paper, we present an analysis of the named entities in a publicly-available tweet dataset in Turkish with respect to their being name variants belonging to different categories. We also provide finer-grained annotations of the named entities as well-formed names and different categories of name variants, where these annotations are made publicly-available. The analysis presented and the accompanying annotations will contribute to related research on the treatment of named entities in social media.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Social media texts differ from regular texts in various aspects. background 1.00 0.05 0.02 asserted 1.00
s1 One of the main differences is the common use of informal name variants instead of well-formed named entities in social media compared to regular texts. background 1.00 0.08 0.06 asserted 1.00
s2 These name variants may come in the form of abbreviations, nicknames, contractions, and hypocoristic uses, in addition to names distorted due to capitalization and writing errors. background 1.00 0.06 0.03 hedged possibility 1.00
s3 In this paper, we present an analysis of the named entities in a publicly-available tweet dataset in Turkish with respect to their being name variants belonging to different categories. contribution 0.99 0.95 0.05 asserted 0.97
s4 We also provide finer-grained annotations of the named entities as well-formed names and different categories of name variants, where these annotations are made publicly-available. contribution 0.98 0.95 0.07 asserted 1.00
s5 The analysis presented and the accompanying annotations will contribute to related research on the treatment of named entities in social media. interpretation 0.57 0.24 0.24 asserted 0.55

What kind of paper the classifier reports this is: dataset or benchmark 0.74 · evidence from analysis of existing data 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Social media texts differ from regular texts in various aspects. One of the main differences is the common use of informal name variants instead of well-formed named entities in social media compared to regular texts. These name variants may come in the form of abbreviations, nicknames, contractions, and hypocoristic uses, in addition to names distorted due to capitalization and writing errors. In this paper, we present an analysis of the named entities in a publicly-available tweet dataset in Turkish with respect to their being name variants belonging to different categories. We also provide finer-grained annotations of the named entities as well-formed names and different categories of name variants, where these annotations are made publicly-available. The analysis presented and the accompanying annotations will contribute to related research on the treatment of named entities in social media.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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