148 citations · 212 across the 11 of their papers we have counts for
11 papers
ChipLingo: A Systematic Training Framework for Large Language Models in EDA
Lei Li, Xingwen Yu, Jianguo Ni +4
With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-driven engineering domain. Al…
Improving Neural Ranking Models with Traditional IR Methods
Anik Saha, Oktie Hassanzadeh, Alex Gittens +3
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercia…
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Anik Saha, Oktie Hassanzadeh, Alex Gittens +3
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understan…
A Generative Model for Relation Extraction and Classification
Jian Ni, Gaetano Rossiello, Alfio Gliozzo +1
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question an…
Cross-Lingual Relation Extraction with Transformers
Jian Ni, Taesun Moon, Parul Awasthy +1
Relation extraction (RE) is one of the most important tasks in information extraction, as it provides essential information for many NLP applications. In this paper, we propose a c…
Cascaded Models for Better Fine-Grained Named Entity Recognition
Parul Awasthy, Taesun Moon, Jian Ni +1
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has…
Event Presence Prediction Helps Trigger Detection Across Languages
Parul Awasthy, Tahira Naseem, Jian Ni +2
The task of event detection and classification is central to most information retrieval applications. We show that a Transformer based architecture can effectively model event extr…
Towards Lingua Franca Named Entity Recognition with BERT
Taesun Moon, Parul Awasthy, Jian Ni +1
Information extraction is an important task in NLP, enabling the automatic extraction of data for relational database filling. Historically, research and data was produced for Engl…
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Jian Ni, Radu Florian
Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-a…
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
Jian Ni, Georgiana Dinu, Radu Florian
The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. H…
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Jian Ni, Radu Florian
The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities…