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.
The prognostic significance of intratumoral natural killer cells in patients with colorectal carcinoma
Countries citing papers authored by David Martínez
Since
Specialization
Citations
This map shows the geographic impact of David Martínez'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 David Martínez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Martínez more than expected).
This network shows the impact of papers produced by David Martínez. 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 David Martínez. The network helps show where David Martínez may publish in the future.
Co-authorship network of co-authors of David Martínez
This figure shows the co-authorship network connecting the top 25 collaborators of David Martínez.
A scholar is included among the top collaborators of David Martínez 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 David Martínez. David Martínez is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mollá, Diego, et al.. (2015). Document distance for the automated expansion of relevance judgements for information retrieval evaluation. arXiv (Cornell University). 1–4.2 indexed citations
3.
Martínez, David, et al.. (2013). Helping General Physical Educators and Adapted Physical Educators Address the Office of Civil Rights Dear Colleague Guidance Letter: Part III--Practitioners and Programs.. 84(8). 27–35.1 indexed citations
4.
MacKinlay, Andrew, David Martínez, Antonio Jimeno Yepes, et al.. (2013). Extracting Biomedical Events and Modifications Using Subgraph Matching with Noisy Training Data. Meeting of the Association for Computational Linguistics. 35–44.9 indexed citations
5.
Martínez, David, Andrew MacKinlay, Diego Mollá, Lawrence Cavedon, & Karin Verspoor. (2012). Simple similarity-based question answering strategies for biomedical text. 1178. 1–13.4 indexed citations
6.
Martínez, David, et al.. (2012). NICTA and UBC at the TREC 2012 Medical Track. Text REtrieval Conference.2 indexed citations
7.
Sanderson, Mark, et al.. (2012). Using Meta-data to Search for Clinical Records: RMIT at TREC 2012 Medical Track. Text REtrieval Conference.2 indexed citations
8.
Sanderson, Mark, et al.. (2011). Search for Clinical Records: RMIT at Medical TREC.. Text REtrieval Conference.1 indexed citations
9.
Baldwin, Timothy, David Martínez, Su Nam Kim, et al.. (2010). Intelligent Linux Information Access by Data Mining: the ILIAD Project. North American Chapter of the Association for Computational Linguistics. 1(1). 15–16.8 indexed citations
10.
Baldwin, Timothy, et al.. (2009). Web scraping made simple with sitescraper.13 indexed citations
11.
Agirre, Eneko, Timothy Baldwin, & David Martínez. (2008). Improving Parsing and PP Attachment Performance with Sense Information. Meeting of the Association for Computational Linguistics. 317–325.43 indexed citations
12.
Baldwin, Timothy, Su Nam Kim, Francis Bond, et al.. (2008). MRD-based Word Sense Disambiguation: Further Extending Lesk. DR-NTU (Nanyang Technological University). 775–780.11 indexed citations
13.
Martínez, David, Sarvnaz Karimi, Lawrence Cavedon, & Tim Baldwin. (2008). Facilitating biomedical systematic reviews using text classification and ranked retrieval. RMIT Research Repository (RMIT University Library). 53–60.7 indexed citations
14.
Martínez, David, Sarvnaz Karimi, Lawrence Cavedon, & Timothy Baldwin. (2008). Facilitating biomedical systematic reviews using ranked text retrieval and classification.14 indexed citations
15.
Agirre, Eneko, et al.. (2006). Exploring feature set combinations for WSD. Procesamiento del lenguaje natural. 37(37). 285–292.2 indexed citations
16.
Martínez, David. (2005). Supervised Word Sense Disambiguation: Facing current challenges. Procesamiento del lenguaje natural. 34(34). 125–126.7 indexed citations
17.
Agirre, Eneko, et al.. (2004). The Basque lexical-sample task. Meeting of the Association for Computational Linguistics. 1–4.3 indexed citations
18.
Agirre, Eneko & David Martínez. (2004). Unsupervised WSD based on Automatically Retrieved Examples: The Importance of Bias. Empirical Methods in Natural Language Processing. 25–32.37 indexed citations
19.
Martínez, David & Eneko Agirre. (2004). The Effect of Bias on an Automatically-built Word Sense Corpus. Language Resources and Evaluation.5 indexed citations
20.
Agirre, Eneko & David Martínez. (2004). The Basque Country University system: English and Basque tasks. Meeting of the Association for Computational Linguistics. 44–48.19 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.