Search Results for author:
Found 25 papers, 10 papers with code
Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques
We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection.
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages.
Overcoming the challenges in morphological annotation of Turkish in universal dependencies framework
This paper presents these issues and our proposals to more accurately represent morphosyntactic information for Turkish while adhering to guidelines of UD.
Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text
It achieved a precision of 88. 37%, a recall of 85. 14%, and an F1-score of 86. 49% on the LLL dataset.
PEAK: Explainable Privacy Assistant through Automated Knowledge Extraction
The generated explanations can be used by users to understand the recommendations of the privacy assistant.
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting.
Exploiting Pretrained Biochemical Language Models for Targeted Drug Design
On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations.
Enhancements to the BOUN Treebank Reflecting the Agglutinative Nature of Turkish
In this study, we aim to offer linguistically motivated solutions to resolve the issues of the lack of representation of null morphemes, highly productive derivational processes, and syncretic morphemes of Turkish in the BOUN Treebank without diverging from the Universal Dependencies framework.
Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions
By factoring in the user's own understanding of privacy, such as risk factors or own labels, the personal assistant can personalize its recommendations per user.
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts
For low-resource languages such as Turkish, there is a lack of such annotated data.
Cluster-based Mention Typing for Named Entity Disambiguation
At the named entity disambiguation phase, first the cluster-based types of a given mention are predicted and then, these types are used as features in a ranking model to select the best entity among the candidates.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Here, we introduce the application of balancing loss functions for multi-label text classification.
Ranked #1 on
Multi-Label Text Classification
on Reuters-21578
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models.
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification
Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering.
Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool
In addition, we report the parsing results of a state-of-the-art dependency parser obtained over the BOUN Treebank as well as two other treebanks in Turkish.
Cultural Vocal Bursts Intensity Prediction
Dependency Parsing
A Hybrid Approach to Dependency Parsing: Combining Rules and Morphology with Deep Learning
Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser.
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.
WideDTA: prediction of drug-target binding affinity
In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model.
ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction
Our aim is to process the patterns in SMILES as a language to predict protein-ligand affinity, even when we cannot infer the function from the sequence.
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts.
DeepDTA: Deep Drug-Target Binding Affinity Prediction
The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.
Ranked #1 on
Drug Discovery
on BindingDB IC50
A novel methodology on distributed representations of proteins using their interacting ligands
We showed that ligand-based protein representation, which uses only SMILES strings of the ligands that proteins bind to, performs as well as protein-sequence based representation methods in protein clustering.
BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain
A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods.
Ranked #8 on
Sentence Embeddings For Biomedical Texts
on BIOSSES
(using extra training data)
