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.
SemEval-2016 Task 5: Aspect Based Sentiment Analysis
2016799 citationsSuresh Manandhar, Natalia Loukachevitch et al.profile →
Citations per year, relative to Natalia Loukachevitch Natalia Loukachevitch (= 1×)
peers
Haris Papageorgiou
Countries citing papers authored by Natalia Loukachevitch
Since
Specialization
Citations
This map shows the geographic impact of Natalia Loukachevitch'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 Natalia Loukachevitch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natalia Loukachevitch more than expected).
Fields of papers citing papers by Natalia Loukachevitch
This network shows the impact of papers produced by Natalia Loukachevitch. 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 Natalia Loukachevitch. The network helps show where Natalia Loukachevitch may publish in the future.
Co-authorship network of co-authors of Natalia Loukachevitch
This figure shows the co-authorship network connecting the top 25 collaborators of Natalia Loukachevitch.
A scholar is included among the top collaborators of Natalia Loukachevitch 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 Natalia Loukachevitch. Natalia Loukachevitch is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Loukachevitch, Natalia, et al.. (2021). Using ontology for natural sciences and technologies for vacancies analysis. CEUR Workshop Proceedings. 2090. 30–38.2 indexed citations
Loukachevitch, Natalia, et al.. (2018). Extracting Sentiment Attitudes from Analytical Texts via Piecewise Convolutional Neural Network.. 186–192.3 indexed citations
9.
Loukachevitch, Natalia, et al.. (2017). Russian-Tatar Socio-political Thesaurus: Publishing in the Linguistic Linked Open Data Cloud. International journal of open information technologies. 5(11). 64–73.2 indexed citations
Loukachevitch, Natalia, et al.. (2016). Creating a General Russian Sentiment Lexicon. Language Resources and Evaluation. 1171–1176.35 indexed citations
Loukachevitch, Natalia, et al.. (2014). Summarizing News Clusters on the Basis of Thematic Chains. Language Resources and Evaluation. 1600–1607.2 indexed citations
14.
Loukachevitch, Natalia, et al.. (2012). Extraction of Russian Sentiment Lexicon for Product Meta-Domain. International Conference on Computational Linguistics. 593–610.19 indexed citations
15.
Loukachevitch, Natalia. (2012). Automatic Term Recognition Needs Multiple Evidence. Language Resources and Evaluation. 2401–2407.10 indexed citations
16.
Loukachevitch, Natalia, et al.. (2006). Development of Linguistic Ontology on Natural Sciences and Technology. Language Resources and Evaluation. 1077–1082.11 indexed citations
Loukachevitch, Natalia, et al.. (2004). Development of Ontologies with Minimal Set of Conceptual Relations. Language Resources and Evaluation.3 indexed citations
19.
Loukachevitch, Natalia, et al.. (2000). Thesaurus-Based Structural Thematic Summary in Multilingual Information Systems - Machne Translation Review. Machine Translation. 10–20.7 indexed citations
20.
Loukachevitch, Natalia, et al.. (1997). Conceptual Indexing Using Thematic Representation of Texts.. Text REtrieval Conference. 403–413.6 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.