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Samuel Mensah

Samuel Mensah has 4 papers in our library, in 2019–2026, most of them on Information extraction, Sentiment & emotion, Evaluation & benchmarks; often with Charese Smiley, Elena Kochkina, Jabez Magomere.

4 papers in our library · 2019–2026

OpenAlex · h-index 0 · 9 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 71 times in our library · ⭐ Paper Stars ranking: #3822

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢JPMorgan Chase & CoNew York · United States2026 · 4 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 4Research subjects and languages the work is about.Sentiment & emotion · 1 workReasoning · 1 workEvaluation & benchmarks · 1 workInformation extraction · 1 work

 Works by year

2020s

20262 papers20251 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (2019 · 499 citations)
    The paper addresses aspect-based sentiment classification: given a sentence and one aspect expression in it (for example, a dish in a restaurant review), decide whether the opinion about that aspect is positive, negative or neutral.
  2. FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking (2025 · 3 citations)
    Natural language inference has had little attention in financial NLP. Without a dedicated benchmark, nobody could measure how well general or finance-tuned models judge whether one financial statement follows from, contradicts, or is unrelated to another. The authors built FinNLI to fill this gap.
  3. Distill and Align Decomposition for Enhanced Claim Verification (2026)
    The paper tackles a weak spot in decompose-then-verify fact-checking of long LLM outputs. The step that splits sentences into subclaims is usually tuned separately from the verifier. It can therefore produce claims that are too fragmented, too coarse, or missing context for the verifier to judge well. The authors…
  4. ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images (2026)
    Generalist vision-language models (VLMs) score well on standard document understanding benchmarks. It is less clear whether they can pull many related fields out of varied business documents and arrange them according to a schema the user defines on the fly. Existing resources do not test this well. KEE and RE…

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 71 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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