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.
Self organization of a massive document collection
2000560 citationsTeuvo Kohonen, Samuel Kaski et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Krista Lagus'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 Krista Lagus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Krista Lagus more than expected).
This network shows the impact of papers produced by Krista Lagus. 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 Krista Lagus. The network helps show where Krista Lagus may publish in the future.
Co-authorship network of co-authors of Krista Lagus
This figure shows the co-authorship network connecting the top 25 collaborators of Krista Lagus.
A scholar is included among the top collaborators of Krista Lagus 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 Krista Lagus. Krista Lagus 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.
Mäkelä, Eetu, Krista Lagus, Leo Lahti, et al.. (2020). Wrangling with Non-Standard Data. SHILAP Revista de lepidopterología. 3(1). 81–96.7 indexed citations
Lagus, Krista, Mika Pantzar, & Minna Ruckenstein. (2015). Keskustelun tunneaallot - Suomi24-hanke. Doria (University of Helsinki).1 indexed citations
5.
Virpioja, Sámi, et al.. (2011). Evaluating the effect of word frequencies in a probabilistic generative model of morphology. DSpace repository (University of Tartu). 230–237.6 indexed citations
6.
Kurimo, Mikko, Sámi Virpioja, Ville Turunen, & Krista Lagus. (2010). Morpho Challenge competition 2005--2010: evaluations and results. Meeting of the Association for Computational Linguistics. 87–95.20 indexed citations
7.
Kurimo, Mikko, Sámi Virpioja, Ville Turunen, & Krista Lagus. (2010). Morpho Challenge 2005-2010: Evaluations and Results. Meeting of the Association for Computational Linguistics. 87–95.17 indexed citations
8.
Virpioja, Sámi, et al.. (2010). Semi-Supervised Learning of Concatenative Morphology. Meeting of the Association for Computational Linguistics. 78–86.41 indexed citations
9.
Honkela, Timo, et al.. (2010). GICA: Grounded Intersubjective Concept Analysis - A Method for Enhancing Mutual Understanding and Participation. Aaltodoc (Aalto University).2 indexed citations
10.
Virpioja, Sámi, et al.. (2010). Semi-supervised extensions to Morfessor Baseline.6 indexed citations
11.
Kurimo, Mikko & Krista Lagus. (2007). An Efficiently Focusing Large Vocabulary Language Model.1 indexed citations
12.
Kurimo, Mikko, Mathias Creutz, & Krista Lagus. (2006). Proceedings of the PASCAL Challenge Workshop on Unsupervised segmentation of words into morphemes.11 indexed citations
13.
Creutz, Mathias, Krista Lagus, Krister Lindén, & Sámi Virpioja. (2005). Morfessor and Hutmegs : Unsupervised Morpheme Segmentation for Highly-Inflecting and Compounding Languages. Työväentutkimus Vuosikirja.16 indexed citations
Lagus, Krista, Timo Honkela, Samuel Kaski, & Teuvo Kohonen. (1999). Websom for Textual Data Mining. Artificial Intelligence Review. 13(5-6). 345–364.64 indexed citations
18.
Lagus, Krista. (1998). Generalizability of the WEBSOM Method to Document Collections of Various Types.3 indexed citations
19.
Kaski, Samuel, Krista Lagus, Timo Honkela, & Teuvo Kohonen. (1998). Statistical Aspects of the WEBSOM System in Organizing Document Collections.16 indexed citations
20.
Lagus, Krista, Timo Honkela, Samuel Kaski, & Teuvo Kohonen. (1996). Self-organizing maps of document collections: A new approach to interactive exploration. Knowledge Discovery and Data Mining. 238–243.111 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.