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Found 13 papers, 0 papers with code
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.
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.
Findings of the Covid-19 MLIA Machine Translation Task
This work presents the results of the machine translation (MT) task from the Covid-19 MLIA @ Eval initiative, a community effort to improve the generation of MT systems focused on the current Covid-19 crisis.
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.
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.
Joint Event Detection and Entity Resolution: a Virtuous Cycle
Clustering web documents has numerous applications, such as aggregating news articles into meaningful events, detecting trends and hot topics on the Web, preserving diversity in search results, etc.
Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms
This paper reports on an approach and experiments to automatically build a cross-lingual multi-word entity resource.
Observing Trends in Automated Multilingual Media Analysis
Any large organisation, be it public or private, monitors the media for information to keep abreast of developments in their field of interest, and usually also to become aware of positive or negative opinions expressed towards them.
Clustering of Multi-Word Named Entity variants: Multilingual Evaluation
Multi-word entities, such as organisation names, are frequently written in many different ways.
Named Entity Recognition on Turkish Tweets
Various recent studies show that the performance of named entity recognition (NER) systems developed for well-formed text types drops significantly when applied to tweets.
Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts
We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data.
