Search Results for author: Yannis Papanikolaou

Found 9 papers, 3 papers with code

Slot Filling for Biomedical Information Extraction

2 code implementations BioNLP (ACL) 2022 Yannis Papanikolaou, Marlene Staib, Justin Grace, Francine Bennett

In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings.

Named Entity Recognition Passage Retrieval +3

Teach me how to Label: Labeling Functions from Natural Language with Text-to-text Transformers

1 code implementation18 Jan 2021 Yannis Papanikolaou

Annotated data has become the most important bottleneck in training accurate machine learning models, especially for areas that require domain expertise.

Code Generation Semantic Parsing

DARE: Data Augmented Relation Extraction with GPT-2

no code implementations6 Apr 2020 Yannis Papanikolaou, Andrea Pierleoni

Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues.

Relation Extraction

Deep Bidirectional Transformers for Relation Extraction without Supervision

no code implementations WS 2019 Yannis Papanikolaou, Ian Roberts, Andrea Pierleoni

We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base.

Language Modelling Relation Extraction +1

Neural Embedding Allocation: Distributed Representations of Topic Models

no code implementations10 Sep 2019 Kamrun Naher Keya, Yannis Papanikolaou, James R. Foulds

Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents.

Document Embedding Topic Models

Subset Labeled LDA for Large-Scale Multi-Label Classification

no code implementations16 Sep 2017 Yannis Papanikolaou, Grigorios Tsoumakas

We conduct extensive experiments on eight data sets, with label sets sizes ranging from hundreds to hundreds of thousands, comparing our proposed algorithm with the previously proposed LLDA algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme multi-label classification.

Classification Extreme Multi-Label Classification +3

Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models

no code implementations18 Apr 2017 Yannis Papanikolaou, Grigorios Tsoumakas, Manos Laliotis, Nikos Markantonatos, Ioannis Vlahavas

Background: In this paper we present the approaches and methods employed in order to deal with a large scale multi-label semantic indexing task of biomedical papers.

General Classification Multi-Label Classification

Hierarchical Partitioning of the Output Space in Multi-label Data

no code implementations19 Dec 2016 Yannis Papanikolaou, Ioannis Katakis, Grigorios Tsoumakas

Hierarchy Of Multi-label classifiers (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and secondly employing a given base multi-label classifier (MLC) to the resulting sub-problems.

Multi-Label Classification Multi-Label Learning

Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA

1 code implementation8 May 2015 Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas

We introduce a novel approach for estimating Latent Dirichlet Allocation (LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full conditional distributions over the latent variable assignments to efficiently average over multiple samples, for little more computational cost than drawing a single additional collapsed Gibbs sample.

Multi-Label Classification

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