General-purpose named entity recognition using transformer-based fine-tuned approach

Kannada Named Entity Recognition Using Deep Learning Techniques

Named Entity Recognition (NER) is a natural language processing task concerned with identifying mentions of named entities and classifying them according to a predefined set of categories. Despite the success of NER in domains, where such data is abundant it remains a formidable challenge for low-resource languages such as Kannada. In this paper we discuss the possible ways to approach NER for the Kannada language. We explore various research directions including rule-based methods statistical machine learning neural networks and transformers based tagging methodologies. We highlight the various challenges in achieving NER for such a language and propose a transformer based contextual tagging framework for labelling sequences. We propose to use mBERT IndicBERT and XLM-RoBERTa language models pretrained on target and other related Indic language corpora and further fine-tune these models for the NER task. We discuss various aspects for experimentation including data collection labelling data preparation methods data-splits evaluation metrics comparison with other models hyper parameter tuning entity-wise analysis and error analysis.

Optimizing sample selection for large language model-based entity matching using AssistEM

AssistEM, a framework for efficient LLM adaptation to EM via principled data selection, demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models.

A Comparative Performance Evaluation of Named Entity Recognition using spaCy and NLTK on Benchmark and Custom Datasets

With the rapid growth of digital text data, automatically extracting meaningful information from unstructured text has become essential for applications such as information retrieval, knowledge extraction, and question answering. Named Entity Recognition (NER) is a key task in Natural Language Processing (NLP) that identifies and classifies entities such as persons, organizations, locations, dates, and monetary values into predefined categories. A large portion of real-world data is unstructured, making NER essential for extracting structured information. This study evaluates two widely used Python-based NLP libraries, spaCy and NLTK, on a benchmark dataset (CoNLL-2003) and a custom news dataset from Kaggle. Performance was measured using precision, recall, and F1-score. Results show that spaCy outperforms NLTK in both accuracy and efficiency, especially for real-world text data, demonstrating its suitability for robust and scalable NER applications. BERT-based and transformer-based models are discussed in the related work section to provide insight into recent advancements in Named Entity Recognition techniques.

Domain-Specific Text Embedding Models for Entity Resolution

General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person. This limitation affects applications such as entity resolution and duplicate record retrieval, where small textual differences may either preserve or change identity. This paper investigates whether domain-specific triplet fine-tuning can adapt pretrained embedding models for identity-sensitive retrieval. A synthetic dataset of business and person records was created with identity-preserving variations and challenging non-matching examples. Two widely used embedding models were evaluated before and after fine-tuning using a margin-based similarity evaluation. The results show substantial improvements in separating true matches from highly similar non-matches, demonstrating that domain-specific triplet training can effectively reshape general-purpose embedding spaces for entity retrieval. These findings suggest that targeted fine-tuning provides a practical approach for improving embedding models in data quality management and information retrieval applications.

A Transformer-Based Model for Named Entity Recognition in Winning Bid Text

Enhancing scientific named entity recognition via large language models: a type-driven multi-task learning approach

Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) have demonstrated the capacity to achieve competitive SciNER performance with minimal human effort. Existing research highlights the importance of incorporating candidate entity type information for accurate entity recognition and classification by LLMs. However, when too many candidate entity types are provided in the prompt, LLMs struggle to accurately recognize and label entities in scientific texts, where entity types are more complex than in general domains. To address this challenge, we propose TdSciNER, a type-driven approach that effectively leverages entity type information to enhance SciNER performance. In TdSciNER, we first design an entity type filter model to identify the most likely entity types present in a given sentence. Subsequently, we introduce an auxiliary multi-class entity typing task within a multi-task learning framework alongside SciNER to obtain richer contextual representations. Then, we develop a novel demonstration selection strategy based on sentence similarity and entity type diversity to activate the in-context learning capabilities of LLMs, thereby improving entity recognition accuracy across diverse scientific domains. Experiments on three datasets demonstrate that our method achieves performance comparable to fully supervised models. Further analysis validates that each entity type-driven component in TdSciNER contributes to the improvement of SciNER performance. This work provides valuable insights for future advancements in SciNER and broader information extraction tasks in scientific text mining.