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.
A Decomposable Attention Model for Natural Language Inference
2016772 citationsAnkur P. Parikh, Oscar Täckström et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Oscar Täckström
Since
Specialization
Citations
This map shows the geographic impact of Oscar Täckström'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 Oscar Täckström with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oscar Täckström more than expected).
This network shows the impact of papers produced by Oscar Täckström. 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 Oscar Täckström. The network helps show where Oscar Täckström may publish in the future.
Co-authorship network of co-authors of Oscar Täckström
This figure shows the co-authorship network connecting the top 25 collaborators of Oscar Täckström.
A scholar is included among the top collaborators of Oscar Täckström 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 Oscar Täckström. Oscar Täckström is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Reddy, Siva, Oscar Täckström, Slav Petrov, Mark Steedman, & Mirella Lapata. (2017). Universal Semantic Parsing. Edinburgh Research Explorer. 89–101.39 indexed citations
3.
Parikh, Ankur P., Oscar Täckström, Dipanjan Das, & Jakob Uszkoreit. (2016). A Decomposable Attention Model for Natural Language Inference. 2249–2255.772 indexed citations breakdown →
Täckström, Oscar, Ryan McDonald, & Joakim Nivre. (2013). Target Language Adaptation of Discriminative Transfer Parsers. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1061–1071.65 indexed citations
8.
McDonald, Ryan, Joakim Nivre, Yoav Goldberg, et al.. (2013). Universal Dependency Annotation for Multilingual Parsing. Meeting of the Association for Computational Linguistics. 2. 92–97.260 indexed citations
Täckström, Oscar, Ryan McDonald, & Jakob Uszkoreit. (2012). Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure. KTH Publication Database DiVA (KTH Royal Institute of Technology). 477–487.139 indexed citations
11.
Täckström, Oscar. (2012). Nudging the Envelope of Direct Transfer Methods for Multilingual Named Entity Recognition. KTH Publication Database DiVA (KTH Royal Institute of Technology). 55–63.13 indexed citations
12.
Täckström, Oscar & Ryan McDonald. (2011). Semi-supervised latent variable models for sentence-level sentiment analysis. KTH Publication Database DiVA (KTH Royal Institute of Technology). 569–574.52 indexed citations
Täckström, Oscar, Sumithra Velupillai, Martin Hassel, et al.. (2010). Uncertainty Detection as Approximate Max-Margin Sequence Labelling. KTH Publication Database DiVA (KTH Royal Institute of Technology). 84–91.3 indexed citations
Karlgren, Jussi, Gunnar Eriksson, & Oscar Täckström. (2008). SICS at NTCIR-7 MOAT: Constructions represented in parallel with lexical items. 237–240.2 indexed citations
18.
Täckström, Oscar, Cecilia Bergh, Magnus Sahlgren, et al.. (2008). An Embodied question answering system for use in the treatment of eating disorders. KTH Publication Database DiVA (KTH Royal Institute of Technology).1 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.