token-classification
43 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in token-classification
Most implemented papers
WangchanBERTa: Pretraining transformer-based Thai Language Models
vistec-AI/thai2transformers
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However, for a relatively low-resource language such as Thai, the choices of models are limited to training a BERT-based model based on a much smaller dataset or finetuning multi-lingual models, both of which yield suboptimal downstream performance.
Retrieval Augmented Generation using Engineering Design Knowledge
siddharthl93/engineering-design-knowledge
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For this task, we create a dataset of 375, 084 examples and fine-tune language models for relation identification (token classification) and elicitation (sequence-to-sequence).
Label Supervised LLaMA Finetuning
We evaluate this approach with Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU.
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems.
Embedded Named Entity Recognition using Probing Classifiers
nicpopovic/stoke
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Streaming text generation has become a common way of increasing the responsiveness of language model powered applications, such as chat assistants.
Evaluating Named Entity Recognition: A comparative analysis of mono- and multilingual transformer models on a novel Brazilian corporate earnings call transcripts dataset
Our findings provide insights into the differing performance of BERT- and T5-based models for the NER task.
POS-tagging to highlight the skeletal structure of sentences
This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification.
Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
samuelbroscheit/entity_knowledge_in_bert
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CONLL 2019
We show on an entity linking benchmark that (i) this model improves the entity representations over plain BERT, (ii) that it outperforms entity linking architectures that optimize the tasks separately and (iii) that it only comes second to the current state-of-the-art that does mention detection and entity disambiguation jointly.
Common-Knowledge Concept Recognition for SEVA
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering.
Joint Learning of Syntactic Features Helps Discourse Segmentation
takshakpdesai/discourse-segmenter
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LREC 2020
This paper describes an accurate framework for carrying out multi-lingual discourse segmentation with BERT (Devlin et al., 2019).
