Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Citations per year, relative to Ahmet Aker Ahmet Aker (= 1×)
peers
Svitlana Volkova
Countries citing papers authored by Ahmet Aker
Since
Specialization
Citations
This map shows the geographic impact of Ahmet Aker's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ahmet Aker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ahmet Aker more than expected).
This network shows the impact of papers produced by Ahmet Aker. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ahmet Aker. The network helps show where Ahmet Aker may publish in the future.
Co-authorship network of co-authors of Ahmet Aker
This figure shows the co-authorship network connecting the top 25 collaborators of Ahmet Aker.
A scholar is included among the top collaborators of Ahmet Aker based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ahmet Aker. Ahmet Aker is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Aker, Ahmet, et al.. (2019). Good , Neutral or Bad - News Classification.. International ACM SIGIR Conference on Research and Development in Information Retrieval. 9–14.1 indexed citations
2.
Aker, Ahmet, et al.. (2019). Corpus of News Articles Annotated with Article Level Sentiment.. International ACM SIGIR Conference on Research and Development in Information Retrieval. 30–35.4 indexed citations
Aker, Ahmet, et al.. (2018). Multi-lingual Argumentative Corpora in English, Turkish, Greek, Albanian, Croatian, Serbian, Macedonian, Bulgarian, Romanian and Arabic. Language Resources and Evaluation.2 indexed citations
5.
Aker, Ahmet, et al.. (2018). Can Rumour Stance Alone Predict Veracity. International Conference on Computational Linguistics. 3360–3370.35 indexed citations
6.
Barker, Emma, Monica Lestari Paramita, Adam Funk, et al.. (2016). What's the issue here?: Task-based evaluation of reader comment summarization systems. Language Resources and Evaluation. 3094–3101.3 indexed citations
7.
Aker, Ahmet, Monica Lestari Paramita, Mārcis Pinnis, & Robert Gaizauskas. (2014). Bilingual dictionaries for all EU languages. Language Resources and Evaluation. 2839–2845.8 indexed citations
8.
Aker, Ahmet, Monica Lestari Paramita, Emma Barker, & Robert Gaizauskas. (2014). Bootstrapping Term Extractors for Multiple Languages. Language Resources and Evaluation. 483–489.5 indexed citations
9.
Aker, Ahmet, Monica Lestari Paramita, & Robert Gaizauskas. (2013). Extracting bilingual terminologies from comparable corpora. Meeting of the Association for Computational Linguistics. 402–411.28 indexed citations
10.
Skadiņa, Inguna, Ahmet Aker, Bogdan Babych, et al.. (2012). Collecting and Using Comparable Corpora for Statistical Machine Translation. Language Resources and Evaluation. 438–445.22 indexed citations
11.
Aker, Ahmet, et al.. (2012). Assessing Crowdsourcing Quality through Objective Tasks. Language Resources and Evaluation. 1456–1461.31 indexed citations
12.
Aker, Ahmet, Yang Feng, & Robert Gaizauskas. (2012). Automatic Bilingual Phrase Extraction from Comparable Corpora. International Conference on Computational Linguistics. 23–32.11 indexed citations
13.
Aker, Ahmet, Evangelos Kanoulas, & Robert Gaizauskas. (2012). A light way to collect comparable corpora from the Web. Language Resources and Evaluation. 15–20.10 indexed citations
14.
Paramita, Monica Lestari, Paul Clough, Ahmet Aker, & Robert Gaizauskas. (2012). Correlation between Similarity Measures for Inter-Language Linked Wikipedia Articles. Language Resources and Evaluation. 790–797.6 indexed citations
15.
Wells, Bill, et al.. (2012). A Corpus of Spontaneous Multi-party Conversation in Bosnian Serbo-Croatian and British English. Language Resources and Evaluation. 1323–1327.6 indexed citations
16.
Lloret, Elena, Laura Plaza, & Ahmet Aker. (2011). Multi-Document Summarization by Capturing the Information Users are Interested in. Recent Advances in Natural Language Processing. 77–83.1 indexed citations
17.
Aker, Ahmet, Trevor Cohn, & Robert Gaizauskas. (2010). Multi-Document Summarization Using A* Search and Discriminative Learning. Empirical Methods in Natural Language Processing. 482–491.26 indexed citations
18.
Aker, Ahmet & Robert Gaizauskas. (2010). Generating Image Descriptions Using Dependency Relational Patterns. Meeting of the Association for Computational Linguistics. 1250–1258.63 indexed citations
19.
Aker, Ahmet, et al.. (2009). Correlations of Negative and Positive Symptoms with Brain MRI Findings in Schizophrenia. 22(1). 18–26.1 indexed citations
20.
Aker, Ahmet & Robert Gaizauskas. (2009). Summary Generation for Toponym-referenced Images using Object Type Language Models. Recent Advances in Natural Language Processing. 6–11.11 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.