Recognising non-named spatial entities in literary texts: a novel spatial entities classifier

2024-12-04 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

A novel spatial entities classifier is presented, specifically designed for recognizing "non-named spatial entities" (NNSE) within literary texts. This research details a case study focused on predicting NNSE in a historical corpus composed of Swiss-German novels. The classification system leverages a deep learning model, which is implemented in conjunction with BERT and Prodigy for enhanced performance. The primary objective is to accurately identify spatial references that are not proper nouns, such as common nouns like "the village square" or "the mountain path," within complex textual data. This development provides a specialized tool for digital humanities, enabling more nuanced analysis of geographical and environmental contexts embedded in historical literary works.

Key takeaway

For NLP Engineers or Digital Humanities Researchers developing custom entity recognition models for literary corpora, this work demonstrates a viable approach for identifying non-named spatial entities. You should consider integrating deep learning models with pre-trained language models like BERT and annotation tools such as Prodigy to tackle complex, domain-specific entity types. This method can significantly enhance the granularity of spatial analysis in historical texts.

Key insights

A novel deep learning classifier identifies non-named spatial entities in Swiss-German novels using BERT and Prodigy.

Method

A deep learning model, integrated with BERT and Prodigy, is used to predict non-named spatial entities (NNSE) in a historical Swiss-German novel corpus.

Topics

• Non-named Spatial Entities
• Deep Learning
• BERT
• Prodigy
• Literary Text Analysis
• Entity Recognition

Best for: AI Scientist, NLP Engineer, Research Scientist

Related on AIssential

• Semantic marking of named entities in The Lusiads — Hybrid NER workflows improve accuracy for historical literary texts, overcoming limitations of models trained on modern corpora.
• Predicting Poets' Origins from Verse: A Computational Analysis of Regional Linguistic Fingerprints in the Complete Tang Poems — Tang poets' geographic origins leave detectable linguistic traces in their verse, revealing historical regional divergence.
• Nested Named Entity Recognition in Plasma Physics Research Articles — A lightweight BERT-CRF model with entity-specific specialization and Bayesian Optimization effectively extracts nested entities from plasma physics texts.
• A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research — A RoBERTa classifier, trained on LLM-generated annotations, accurately distinguishes manner and result verbs at scale.
• CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts — HIPE-2026 evaluates person-place relation extraction in historical texts, emphasizing accuracy, efficiency, and generalization.
• How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification — Classical NLP, with careful feature engineering and ensemble stacking, excels at stylistic text classification.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.