most citedOCR Error Post-Correction with LLMs in Historical Documents: No Free Lunches

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models

Amanda Myntti, Jenna Kanerva, Veronika Laippala +1

Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval promp…

cs.CL2026

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance

Amanda Myntti, Jenna Kanerva, Veronika Laippala +1

In this paper, we show that high-performing embedding models organize their embedding spaces in a consistent way. We evaluate 25 contemporary embedding models on five MTEB tasks sp…

cs.CL2026

Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMs

Joonatan Laato, Veera Schroderus, Jenna Kanerva +3

Digitized historical archives make it possible to study everyday social life on a large scale, but the information extracted directly from text often does not directly allow one to…

cs.CV2025

Creating a Historical Migration Dataset from Finnish Church Records, 1800-1920

Ari Vesalainen, Jenna Kanerva, Aida Nitsch +4

This article presents a large-scale effort to create a structured dataset of internal migration in Finland between 1800 and 1920 using digitized church moving records. These record…

cs.CL2025★ 1 cited

Extracting Social Connections from Finnish Karelian Refugee Interviews Using LLMs

Joonatan Laato, Jenna Kanerva, John Loehr +2

We performed a zero-shot information extraction study on a historical collection of 89,339 brief Finnish-language interviews of refugee families relocated post-WWII from Finnish Ea…

cs.CL2025★ 2 cited

OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunches

Jenna Kanerva, Cassandra Ledins, Siiri Käpyaho +1

Optical Character Recognition (OCR) systems often introduce errors when transcribing historical documents, leaving room for post-correction to improve text quality. This study eval…