Radu Florian

One for All: Towards Language Independent Named Entity Linking

2016

Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, …

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

2019 · arXiv (Cornell University)

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments …

Cascaded Models for Better Fine-Grained Named Entity Recognition

2020 · arXiv (Cornell University)

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 been done on datasets with few classes of …

Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks

2020

Transfer learning is a popular technique to learn a task using less training data and fewer compute resources. However, selecting the correct source model for transfer learning is a challenging task. We demonstrate a novel …

Neural Cross-Lingual Coreference Resolution and its Application to\n Entity Linking

2018 · arXiv (Cornell University)

We propose an entity-centric neural cross-lingual coreference model that\nbuilds on multi-lingual embeddings and language-independent features. We\nperform both intrinsic and extrinsic evaluations of our model. In the intrinsic\nevaluation, we show that our model, when trained on …

Bilateral Multi-Perspective Matching for Natural Language Sentences

2017 · arXiv (Cornell University)

Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we …

Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection

2017

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. However, annotating NER data by human is expensive and time-consuming, and …

Neural Cross-Lingual Entity Linking

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which …