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

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

Named Entity Recognition (NER) is a fundamental component of Natural Language Processing. It enables structured understanding of textual data across various domains such as news articles, Wikipedia, government records, social media, and other general data. NER is also useful for down-stream tasks like text summarization (summarizing text by identifying key entities and events), text classification, information retrieval and filtering, question-answering, and many more. The research article proposes a transformer-based fully fine-tuned XLNet model with 117M parameters. The model is designed to recognize and extract named entities form general domain data. A full fine-tuning approach is employed, updating all model parameters. Differential optimization rules are applied to distinct parameter groups, with normalization layer parameters (bias, gamma, beta) excluded from weight decay. This strategy helps to enhance model generalization and decrease the risk of overfitting. A full fine-tuning was employed on transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, XLNet(117M), ALBERT, ELECTRA and ERNIE. In addition, probabilistic deep learning based classifier models like Multinomial Naïve Bayes (MNB) and Bidirectional LSTM-Conditional Random Field (BiLSTM-CRF) were also evaluated. For evaluation, metrics such as F1-score, recall, precision and macro-average values were used, along entity-level evaluation. Overall, transformer-based models outperformed others, achieving higher score on evaluation metrics with strong performance at the entity level. The proposed fully fine-tuned XLNet(117M) model achieved the highest training and testing accuracies of 97.1% for both. Additionally, the proposed framework outperformed other models in terms of evaluation metrics with a precision (97.1%), recall (95.9%), and F1-score (96.5%). Moreover, XLNet(117M) also obtained maximum macro average values in terms of precision (64%), recall (55%) and F1-score (58%) making it the prominent and best performing model. Since the proposed framework is trained on general purpose data, it can be used for content recommendation, information retrieval in Q&A systems. It can also serve as a base-model to use it further in domain specific tasks by transfer-learning.

Speak Beyond English: Multilingual Prompts Improve Query Classification in Small Language Models

This paper tests whether prompting the same (frozen) SLM in three typologically diverse languages and aggregating the outputs can improve classification without retraining or translation, and suggests that cross-lingual diversity rather than surface-level input variation drives the gain.

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

The factors underlying performance differences across matcher architectures are clarified and motivate future research and benchmark designs that better disentangle architectural choices from model-level factors while explicitly evaluating distribution shift and cross-dataset transferability.

Ensemble-Based Approach for Amazigh POS Tagging: Leveraging Multiple Models for Enhanced Performance in Low-Resource Language Processing

Part-of-Speech (POS) tagging is a foundational task in Natural Language Processing (NLP), yet it remains challenging for low-resource and morphologically rich languages such as Amazigh. This paper proposes a hybrid ensemble framework for Amazigh POS tagging that integrates three complementary models: a Bidirectional Long Short-Term Memory network (BiLSTM), a Conditional Random Field model (CRF), and a rule-based morphological analyzer (RBMA). Rather than treating prior results obtained on different corpora and tag inventories as directly comparable, the study evaluates all proposed components under a common 54-tag experimental setting based on the publicly available Amazigh Linguistic Dataset. Three ensemble strategies are examined: majority voting, validation-weighted voting, and logistic-regression stacking. An additional late-fusion ablation applies hard and soft RBMA constraints to CRF and Stacking outputs; hard masking degrades performance substantially, whereas soft masking is more robust but remains below unconstrained decoding. The best micro-level performance is obtained by the stacking ensemble, which reaches 98.51% Micro-F1/accuracy, whereas the boosting-like weighted ensemble obtains the strongest Macro-F1 among the ensemble variants, reaching 74.24%. These results show that hybrid ensemble methods can improve token-level accuracy in low-resource POS tagging, while also revealing a trade-off between frequent-tag accuracy and rare-tag robustness. The findings highlight the usefulness of combining neural, probabilistic, and rule-based information for Amazigh POS tagging, and point to class-balanced meta-learning and character/subword representations as important directions for improving rare and out-of-vocabulary categories.

Automatic Domain Classification of Tabular Datasets Using Large Language Models

Lightweight reasoning models for NER

Lite-CoNER is proposed, a lightweight NER framework that achieves an effective balance between recognition accuracy and inference efficiency and provides a transparent view of the decision-making process, proving that lightweight models can effectively inherit complex logic through structured distillation.