Search Results for author:
Found 27 papers, 4 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.
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings
We tackle the problem of neural headline generation in a low-resource setting, where only limited amount of data is available to train a model.
EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity
In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity).
Multilingual and Multimodal Topic Modelling with Pretrained Embeddings
This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space.
Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change
In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes.
Grammatical Profiling for Semantic Change Detection
Semantics, morphology and syntax are strongly interdependent.
Three-part diachronic semantic change dataset for Russian
We present a manually annotated lexical semantic change dataset for Russian: RuShiftEval.
Scalable and Interpretable Semantic Change Detection
We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods.
Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection
This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection.
Topic modelling discourse dynamics in historical newspapers
This paper addresses methodological issues in diachronic data analysis for historical research.
A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval
We address the problem of linking related documents across languages in a multilingual collection.
Capturing Evolution in Word Usage: Just Add More Clusters?
The way the words are used evolves through time, mirroring cultural or technological evolution of society.
Word Clustering for Historical Newspapers Analysis
This paper is a part of a collaboration between computer scientists and historians aimed at development of novel tools and methods to improve analysis of historical newspapers.
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.
Comparison of Representations of Named Entities for Document Classification
We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification.
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings
We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence.
Benchmarks and models for entity-oriented polarity detection
We address the problem of determining entity-oriented polarity in business news.
HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks
Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news.
Grouping business news stories based on salience of named entities
In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles.
Towards Never Ending Language Learning for Morphologically Rich Languages
This work deals with ontology learning from unstructured Russian text.
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.
Clustering of Russian Adjective-Noun Constructions using Word Embeddings
This paper presents a method of automatic construction extraction from a large corpus of Russian.
