This paper improves NEL for legal documents using transfer learning.
problem Linking named entities in legal documents.
method Transfer learning approach applied to legal datasets.
result Improved F1-score of 98.90% and 98.01% on legal datasets.
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48 results for “Named-Entity Linking”
Paper uses graph structure to improve Wikipedia classification.
problem Classifying Wikipedia into a fine-grained named entity hierarchy.
method Explores graph descriptors and weighted models for feature vectors.
result Graph information reduces sparsity and improves classification.
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.
A graph traversal algorithm for cold-start news recommendation using named entities.
problem Cold-start news recommendation for articles without user-specific information.
method Graph traversal algorithm and novel weighting scheme for named entities over a knowledge graph.
result Our method produces stronger Pearson correlation to human similarity scores than other cold-start methods.
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentationcs.CL
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.
Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easy-to-use named-entity recognition tool…
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 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.
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.
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.
Improves biomedical entity linking with latent type modeling.
problem Lack of fine-grained type information for entity disambiguation.
method Jointly models entity disambiguation and latent type learning without direct supervision.
result Significant performance improvements over state-of-the-art techniques.
MedCAT extracts valuable medical information from unstructured text.
problem Extracting structured information from unstructured biomedical documents.
method Unsupervised machine learning for disambiguation of entities.
result Improved entity detection and linking compared to existing tools.
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.
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…
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddingscs.CL
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.
SoulMate links short texts through multi-aspect embeddings.
problem Linking authors of noisy, ambiguous short texts.
method Neural network-based temporal-textual framework generating author subgraphs.
result Higher performance in linking short-text authors compared to competitors.
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.
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.
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 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.
Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: la…
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.
This study improves the performance of neural named entity recognition by a margin of up to 11% in F-score on the example of a low-resource language like German, thereby outperforming existing baselines and establishing a new state-of-the-art on each single open-source dataset. Rather than designing deeper and wider hy…
Browsing and finding relevant information for Bangladeshi laws is a challenge faced by all law students and researchers in Bangladesh, and by citizens who want to learn about any legal procedure. Some law archives in Bangladesh are digitized, but lack proper tools to organize the data meaningfully. We present a text vi…
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.
Conservative Likelihood Ratio Estimator for Infrequent Data Slightly above a Frequency Thresholdstat.ML
Proposes a conservative LR estimator for infrequent data near a frequency threshold.
problem Overestimation of likelihood ratios for infrequent data near a frequency threshold.
method Conservative likelihood ratio estimator for frequencies slightly above a threshold.
result Improves prediction accuracy in named entity context prediction.
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performan…
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.
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.
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.
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 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.
Paper evaluates word embedding techniques on Russian digital humanities datasets.
problem Evaluating word embedding techniques on Russian language datasets.
method Created and evaluated word intrusion and word analogy tasks on manually curated datasets.
result Baseline evaluations with word embedding models show differences in performance for Russian and English datasets.
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.
New approach detects and ranks novel and developing cyber threats in Twitter.
problem Detecting and ranking novel and developing cyber threats in Twitter streams.
method Unsupervised machine learning approach focusing on novelty and trendiness.
result Ranking of cyber threat events based on importance score using extracted terms.
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.
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.
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.