Search Results for author:
Found 16 papers, 0 papers with code
Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task.
Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021
This paper describes the Shared Task on Fine-grained Event Classification in News-like Text Snippets.
Resources and Experiments on Sentiment Classification for Georgian
The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported.
Exploring Linguistically-Lightweight Keyword Extraction Techniques for Indexing News Articles in a Multilingual Set-up
This paper presents a study of state-of-the-art unsupervised and linguistically unsophisticated keyword extraction algorithms, based on statistic-, graph-, and embedding-based approaches, including, i. a., Total Keyword Frequency, TF-IDF, RAKE, KPMiner, YAKE, KeyBERT, and variants of TextRank-based keyword extraction algorithms.
Cross-lingual Named Entity Corpus for Slavic Languages
The corpus consists of 5 017 documents on seven topics.
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report
This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field.
New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT?
We introduce a new set of benchmark datasets derived from ACLED data for fine-grained event classification and compare the performance of various state-of-the-art models on these datasets, including SVM based on TF-IDF character n-grams and neural context-free embeddings (GLOVE and FASTTEXT) as well as deep learning-based BERT with its contextual embeddings.
TF-IDF Character N-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study
Automating the detection of event mentions in online texts and their classification vis-a-vis domain-specific event type taxonomies has been acknowledged by many organisations worldwide to be of paramount importance in order to facilitate the process of intelligence gathering.
JRC TMA-CC: Slavic Named Entity Recognition and Linking. Participation in the BSNLP-2019 shared task
We report on the participation of the JRC Text Mining and Analysis Competence Centre (TMA-CC) in the BSNLP-2019 Shared Task, which focuses on named-entity recognition, lemmatisation and cross-lingual linking.
The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking.
On Training Classifiers for Linking Event Templates
The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news.
On the Creation of a Security-Related Event Corpus
This paper reports on an effort of creating a corpus of structured information on security-related events automatically extracted from on-line news, part of which has been manually curated.
The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages
The reported evaluation figures reflect the relatively higher level of complexity of named entity-related tasks in the context of processing texts in Slavic languages.
Multi-word Entity Classification in a Highly Multilingual Environment
This paper describes an approach for the classification of millions of existing multi-word entities (MWEntities), such as organisation or event names, into thirteen category types, based only on the tokens they contain.
