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ACL

Conference on Computational Natural Language Learning (CoNLL)

Location: Abu Dhabi , United Arab Emirates

Conference dates: 12/7/2022 - 12/8/2022

Research H-index
20

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 131 73 92 19

Call for Papers

The focus of CoNLL is on theoretically, cognitively and scientifically motivated approaches to computational linguistics, rather than on work driven by particular engineering applications. Such approaches include:

Computational learning theory and other techniques for theoretical analysis of machine learning models for NLP
Models of first, second and bilingual language acquisition by humans
Models of language evolution and change
Computational simulation and analysis of findings from psycholinguistic and neurolinguistic experiments
Analysis and interpretation of NLP models, using methods inspired by cognitive science or linguistics or other methods
Data resources, techniques and tools for scientifically-oriented research in computational linguistics
Connections between computational models and formal languages or linguistic theories
Linguistic typology, translation, and other multilingual work
Theoretically, cognitively and scientifically motivated approaches to text generation

Overview

The scientific conference ranking presented on this page is dedicated to the field of Computer Science and aims to provide a comprehensive and reliable resource for the academic community and industry professionals. This ranking is meticulously compiled by Research.com, one of the leading websites for science research across all major fields since 2014, and is recognized for providing trusted data on scientific contributions, including in the domain of Computer Science.

Each conference's position in the ranking is determined using a distinctive bibliometric score developed by Research.com. This score is calculated by analyzing the estimated h-index along with the number of leading scientists who have presented at the respective conference over the past three years. The methodology ensures an objective assessment that reflects both the influence and academic engagement associated with each event. The Impact Score values included in this ranking have been gathered as of 2024-11-27.

The rigorous process underlying this ranking involved an exhaustive review of over 2,742 conferences. These conferences were selected following a detailed inspection and thorough examination of more than 148,739 scientific documents published in the last three years by 13,184 leading and respected scientists in the area of Computer Science. This ensures both the credibility and depth of the evaluation, reflecting the complex landscape of contemporary Computer Science research.

For a more detailed explanation of the methodology and data sources used in calculating the ranking scores, please refer to our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing at Conference on Computational Natural Language Learning (based on the number of publications) are:

  • Dan Roth (27 papers) published 1 paper at the last edition, 3 less than at the previous edition,
  • Walter Daelemans (15 papers) absent at the last edition,
  • Alessandro Moschitti (12 papers) absent at the last edition,
  • Yuji Matsumoto (12 papers) absent at the last edition,
  • Roi Reichart (12 papers) absent at the last edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing at Conference on Computational Natural Language Learning (based on the number of publications) are:

  • University of Illinois at Urbana–Champaign (30 papers) published 1 paper at the last edition, 1 less than at the previous edition,
  • Stanford University (28 papers) published 2 papers at the last edition, 3 less than at the previous edition,
  • Google (20 papers) absent at the last edition,
  • Microsoft (19 papers) published 3 papers at the last edition the same number as at the previous edition,
  • University of Cambridge (19 papers) published 1 paper at the last edition, 2 less than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2020 edition, 9.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.65% were posted by at least one author from the top 10 institutions publishing at the conference. Another 6.12% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.41% of all publications and 40.82% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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