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Found 17 papers, 2 papers with code
BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data
We observe that joint learning improves the F1 scores on the SemTabFacts and TabFact test sets by 3. 31{\%} and 0. 77{\%}, respectively.
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.
Analyzing ELMo and DistilBERT on Socio-political News Classification
This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews.
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes
Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel).
Ranked #3 on
Medical Concept Normalization
on BB-norm-phenotype
Turkish Tweet Classification with Transformer Encoder
Short-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space.
Turkish Treebanking: Unifying and Constructing Efforts
In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options.
A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative Languages
We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences.
Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization
We introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization.
Segmenting Hashtags using Automatically Created Training Data
Our approach is unsupervised in the sense that instead of using manually segmented hashtags for training the machine learning classifiers, we automatically create our training data by using tweets as well as by automatically extracting hashtag segmentations from a large corpus.
Self-training a Constituency Parser using n-gram Trees
In this study, we tackle the problem of self-training a feature-rich discriminative constituency parser.
