Search Results for author:
Found 27 papers, 9 papers with code
JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approaches
The reverse dictionary task is a sequence-to-vector task in which a gloss is provided as input, and the output must be a semantically matching word vector.
IJS at TextGraphs-16 Natural Language Premise Selection Task: Will Contextual Information Improve Natural Language Premise Selection?
The results demonstrate that the contextual representation is better at capturing meaningful information despite not being pretrained in the mathematical background compared to the statistical approach (e. g., the TF-IDF) with a boost of around 3. 00% MAP@500.
Embeddings models for Buddhist Sanskrit
We release a novel corpus of Buddhist texts, a novel corpus of general Sanskrit and word similarity and word analogy datasets for intrinsic evaluation of Buddhist Sanskrit embeddings models.
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.
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports
We describe initial work into analysing the language used around environmental, social and governance (ESG) issues in UK company annual reports.
EMBEDDIA hackathon report: Automatic sentiment and viewpoint analysis of Slovenian news corpus on the topic of LGBTIQ+
We conduct automatic sentiment and viewpoint analysis of the newly created Slovenian news corpus containing articles related to the topic of LGBTIQ+ by employing the state-of-the-art news sentiment classifier and a system for semantic change detection.
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection
We present a system for zero-shot cross-lingual offensive language and hate speech classification.
Multi-Task Learning for Features Extraction in Financial Annual Reports
For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information.
Semantic change detection for Slovene language: a novel dataset and an approach based on optimal transport
In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers.
The Recent Advances in Automatic Term Extraction: A survey
Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms.
Ensembling Transformers for Cross-domain Automatic Term Extraction
Automatic term extraction plays an essential role in domain language understanding and several natural language processing downstream tasks.
A bilingual approach to specialised adjectives through word embeddings in the karstology domain
We present an experiment in extracting adjectives which express a specific semantic relation using word embeddings.
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?
We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i. e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.
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.
Extending Neural Keyword Extraction with TF-IDF tagset matching
Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics.
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.
COVID-19 therapy target discovery with context-aware literature mining
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert.
Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings
We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings.
TNT-KID: Transformer-based Neural Tagger for Keyword Identification
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity.
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.
Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift
We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings.
Supervised and Unsupervised Neural Approaches to Text Readability
We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents.
Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning Approaches
For the first sub-task, we used a BERT model fine-tuned on the OLID dataset, while for the second and third tasks we developed a custom neural network architecture which combines bag-of-words features and automatically generated sequence-based features.
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
The use of background knowledge is largely unexploited in text classification tasks.
Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing
Despite the significant improvement of data-driven dependency parsing systems in recent years, they still achieve a considerably lower performance in parsing spoken language data in comparison to written data.
