NeuroNER simplifies ANN-based NER for non-experts.
problem Challenging use of ANNs for NER by non-experts.
method Graphical web-based user interface for easy annotation, training, and prediction of entities.
result NeuroNER streamlines NER process for non-expert users.
Improves cross-lingual NER by projecting entities from one language to another.
problem Limited annotated corpora for named entity recognition in many languages.
method Uses machine translation twice: first for sentences, then for entities; matches based on ortho- and phonetic similarity; identifies matches using distributional statistics.
result Improves cross-lingual NER by an average of 4.1 points on 5 diverse languages.
Few-shot NER classification with limited data.
problem Limited annotated data for Named Entity Recognition.
method Prototypical Network + Transfer Learning
result Well-performing classifiers trained on only 20 instances.
Paper proposes neural approach for Chinese named entity recognition.
problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.
Improved named entity recognition in EHRs with transfer learning.
problem Scarce annotated datasets for named entity recognition in EHRs.
method Transfer learning and neural network embeddings.
result 94.6 F1 score in I2B2 2009 Medical Extraction Challenge.
RoBERTa outperforms other pre-trained models in NER tasks.
problem Improving Named Entity Recognition (NER) performance.
method Fine-tuning four pre-trained models (BERT, ERNIE, ERNIE2.0-tiny, RoBERTa) on NER task.
result RoBERTa achieved state-of-the-art results on MSRA-2006 dataset.
Transfer learning improves NER performance on limited labeled data.
problem Label scarcity in patient note de-identification.
method Transfer learning from a large labeled dataset to a smaller labeled dataset.
result Transfer learning enhances NER performance on patient note de-identification.
Greek POS Tagger and Entity Recognizer built using spaCy.
problem Developing a machine learning model for Greek language POS tagging and named entity recognition.
method Machine learning approach with spaCy platform, focusing on morphological features and token classification.
result The Greek spaCy platform improved performance in part-of-speech tagging and named entity recognition.
Quantum analysis tags news for sentiment and entities.
problem Identifying bias in news reporting.
method Continuous data collection, NER and sentiment analysis.
result A corpus of tagged news articles for public use.
Improved neural NER by optimizing large corpora for German.
problem Low-resource language named entity recognition.
method Optimized large corpora, lemmatization, part-of-speech tagging, and detailed optimization.
result Up to 11% improvement in F-score on German NER tasks.
Improved NER in biomedical literature using weakly-supervised augmentation.
problem Challenging NER in scientific literature, especially for biomedical entities.
method Weakly-supervised data augmentation with a reference set.
result Iterative model retraining improves NER performance significantly.
The paper extends influence functions to sequence tagging tasks for better model interpretability.
problem Lack of interpretability methods for sequence tagging models.
method Define and compute influence of training instance segments on test segment predictions.
result The segment influence method tracks with true influence and identifies annotation errors.
A multi-task learning framework improves BioNER performance across different entity types.
problem Limited performance of BioNER systems due to lack of training data for each entity type.
method Multi-task learning framework that collectively uses training data of different entity types.
result Substantially better performance on 15 benchmark BioNER datasets compared to state-of-the-art systems.
Proposes a method to improve deep active learning for NER tasks.
problem Weaknesses of existing deep active learning algorithms in practice.
method Estimates error decay curves of feature-defined subsets to improve sampling efficiency and robustness.
result Significantly outperforms diversification-based methods for black-box NER taggers and makes sampling more robust to labeling noise.
Improved NER in medical text with few examples.
problem Limited annotated examples for NER in medical texts.
method Layer-wise initialization, hyperparameter tuning, pre-training data, custom word embeddings, optimizing OOV words.
result F1 score improved from 69.3% to 78.87%.
This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phra…
Improved NER on Turkish tweets using semi-supervised learning and word embeddings.
problem Named Entity Recognition on informal Turkish text types.
method Semi-supervised learning with neural networks and word embeddings.
result Achieved better F-score performances than previous Turkish NER systems.
System helps engineers with concept recognition for SEVA.
problem Recognizing systems engineering concepts for SEVA.
method Token classification task, domain expert labeling, pre-trained model fine-tuning, essential datasets creation, knowledge graph construction.
result System successfully recognizes systems engineering concepts.
Dynamic Transfer Networks improve NER from clinical notes with less data.
problem Improving named entity recognition from clinical notes with limited data.
method Dynamic Transfer Networks (DTN) using parameter sharing and gating architecture.
result DTN achieves better NER performance with fewer training settings.
Paper presents a method to train NER models without labelled data using weak supervision.
problem Dealing with NER performance drop in new domains without labelled data.
method Weak supervision through automatic annotation and hidden Markov model integration.
result Improvement of about 7 percentage points in entity-level F1 scores. The paper introduces uncertainty quantification for NER models.
problem Current NER models lack uncertainty measures, leading to downstream errors.
method Full-Sequence and Subsequence Conformal Prediction framework.
result The method provides formal guarantees about the reliability of model predictions.
A new method quantizes LSTM gate parameters without performance loss.
problem Quantization loss in LSTM gate parameters without performance degradation.
method Lossy quantization of gate parameters during training, weight parameters adjust to offset quantization loss.
result F1 score decreased by only 0.7% on Named Entity Recognition dataset.
Mobile app improves speech recognition of names with user feedback.
problem Improving speech recognition of proper names on mobile devices.
method Data synthesis and user feedback for keyword-dependent metrics.
result User feedback can significantly improve speech recognition of names.
Improved NER performance on imbalanced data.
problem Highly unbalanced training data in NER tasks.
method Adapted a neural architecture with CRF and BI-LSTM layers, using pre-trained embeddings. Introduced a two-class split to optimize performance.
result Significant improvement in performance for weak classes with minimal training data.
A multi-task learning model for slot tagging in biomedical domains.
problem Limited labeled data, memory constraints, and domain-specific slot types.
method Multi-task learning using deep bidirectional transformers.
result Outperforms previous state-of-the-art systems in efficiency and effectiveness.
Unified BERT model improves NER across multiple languages.
problem Language-specific NER models limit data extraction.
method Jointly trained multilingual BERT with regularization.
result Unified model outperforms monolingual models on various datasets.
Deep neural networks detect cyberthreats from Twitter.
problem Scalable processing of cybersecurity information from social media.
method Convolutional and bidirectional LSTM networks for tweet classification and named entity recognition.
result Pipeline achieves high true positive and F1-score rates for threat detection.
Hungarian text processing improved with efficient, accurate NLP pipelines.
problem Improving text processing for Hungarian language.
method Implemented in spaCy framework, focusing on efficiency and accuracy.
result Near state-of-the-art performance in all text preprocessing steps.
Data mining reveals power structures in Bangladeshi newspapers.
problem Understanding the power dynamics and narrative structure in news reporting.
method Named entity recognition to create temporal actor networks from news statements.
result Cliquishness among powerful political leaders in news articles.
BiLSTM model improves NER and negation detection in radiological reports.
problem Automating medical information extraction from radiological reports.
method Bi-directional Long Short-Term Memory (BiLSTM) neural network architecture.
result BiLSTM outperforms traditional rule-based systems for NER and negation detection.
We apply mixup regularization to RNNs for improved performance.
problem Improving the performance of Recurrent Neural Networks.
method We extend mixup regularization techniques to RNNs, making them easier and less computationally intensive.
result Sequence mixup improved F-1 score and reduced loss in Named Entity Recognition tasks.
Deep learning model improves Vietnamese NER accuracy.
problem Vietnamese named entity recognition (NER) accuracy.
method Combining Bi-LSTM and CRF with word embeddings and semantic features.
result Achieved state-of-the-art NER results on VLSP2016 dataset.
Automated synthesis planning from scientific literature using AI.
problem Accelerate materials design and discovery by connecting scientific literature to synthesis insights.
method Word embeddings from language models, named entity recognition, conditional variational autoencoder.
result The model predicts precursors for perovskite materials using historical data.
QuASE uses QA data to improve non-QA tasks like NER.
problem Can QA data help other NLP tasks?
method Proposes QuASE framework to learn sentence encodings from QA data.
result QuASE encodings improve downstream tasks like NER.
Paper fine-tunes LLaMA-3-8B for financial NER using instruction and LoRA.
problem LLMs struggle with financial NER, especially differentiating entities and amounts.
method Instruction fine-tuning combined with LoRA for parameter-efficient learning.
result Micro-F1 score of 0.894 on financial NER tasks, outperforming other models.
Paper proposes ensemble distillation for well-calibrated structured prediction.
problem Well-calibrated predictions are hard to achieve in structured prediction.
method Ensemble distillation framework for structured prediction.
result Ensemble distillation produces well-calibrated models with similar performance and calibration benefits to ensembles.
Study shows partially-typed NER datasets can match fully-typed ones in model performance.
problem Leveraging multiple partially-typed NER datasets for training models without fully-typed annotations.
method Systematic analysis and controlled experiments comparing partially-typed and fully-typed datasets.
result Models trained with partially-typed annotations can achieve similar performance to those trained with fully-typed annotations.
BloombergGPT is a large language model trained on financial data, outperforming existing models on financial tasks.
problem Lack of specialized large language models for finance.
method Trained on a 363 billion token dataset augmented with 345 billion tokens from general datasets, using a 50 billion parameter model.
result BloombergGPT outperforms existing models on financial tasks without sacrificing performance on general LLM benchmarks.
The paper introduces a method to measure the benefits of incidental supervision signals.
problem Lack of a principled way to measure the benefits of various types of incidental supervision signals.
method Unified PAC-Bayesian motivated informativeness measure, PABI.
result Demonstrates PABI's effectiveness in quantifying the value added by various types of incidental signals.
GTI network learns linguistic features for multi-task sequence tagging.
problem Improving neural model performance on multi-task sequence tagging without explicit features.
method GTI network with neural gate modules to learn relations between tasks.
result GTI network outperforms baselines on chunking and NER tasks.
Optimal Word2Vec hyper-parameters improve NLP tasks.
problem Finding the best Word2Vec hyper-parameters for NLP tasks.
method Empirical evaluation of various hyper-parameter combinations on NLP tasks.
result The best hyper-parameters vary by task, and high analogy scores don't always correlate with performance.
A new challenge and model for multitask NLP.
problem Improving general NLP models across multiple tasks.
method Natural Language Decathlon challenge and Multitask Question Answering Network (MQAN).
result MQAN achieves state-of-the-art performance on multiple tasks.
The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, that is entirely deterministic. We prove that herded Gibbs has an O(1/T) convergence rate for models with independent variables and for ful…
Hotel booking chatbot handles daily tens of thousands of searches.
problem Improving hotel booking through conversational AI.
method Frame-based dialogue management system with ML models for intent, entity recognition, and info retrieval.
result Chatbot deployed on a commercial scale, handling hotel searches daily.
Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the po…
Softmax Q-distribution reveals RAML's theoretical basis in structured prediction.
problem Improving structured prediction models with reward functions.
method Introducing a novel theoretical interpretation of RAML using softmax Q-distribution.
result RAML is equivalent to estimating the softmax Q-distribution, with temperature controlling approximation error.
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop…
OpenTag extracts missing attribute values from product descriptions.
problem Extract missing attribute values from product descriptions.
method Developed a deep tagging model OpenTag using LSTM and CRF, with an attention mechanism and active learning.
result OpenTag discovers new attribute values with minimal human annotation, achieving high F-score.