Search Results for author: Senja Pollak

Found 21 papers, 7 papers with code

Evaluation of contextual embeddings on less-resourced languages

no code implementations22 Jul 2021 Matej Ulčar, Aleš Žagar, Carlos S. Armendariz, Andraž Repar, Senja Pollak, Matthew Purver, Marko Robnik-Šikonja

The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives.

Dependency Parsing

JSI at the FinSim-2 task: Ontology-Augmented Financial Concept Classification

no code implementations17 Jun 2021 Timen Stepišnik Perdih, Senja Pollak, Blaž \v{Skrlj}

The task is to design a system that can automatically classify concepts from the Financial domain into the most relevant hypernym concept in an external ontology - the Financial Industry Business Ontology.

Extending Neural Keyword Extraction with TF-IDF tagset matching

no code implementations31 Jan 2021 Boshko Koloski, Senja Pollak, Blaž Škrlj, Matej Martinc

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.

Keyword Extraction

Identification of COVID-19 related Fake News via Neural Stacking

no code implementations11 Jan 2021 Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, Blaž Škrlj

Identification of Fake News plays a prominent role in the ongoing pandemic, impacting multiple aspects of day-to-day life.

Fake News Detection General Classification

SemEval-2020 Task 3: Graded Word Similarity in Context

no code implementations SEMEVAL 2020 Carlos Santos Armendariz, Matthew Purver, Senja Pollak, Nikola Ljube{\v{s}}i{\'c}, Matej Ul{\v{c}}ar, Ivan Vuli{\'c}, Mohammad Taher Pilehvar

This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish.

Word Similarity

AttViz: Online exploration of self-attention for transparent neural language modeling

1 code implementation12 May 2020 Blaž Škrlj, Nika Eržen, Shane Sheehan, Saturnino Luz, Marko Robnik-Šikonja, Senja Pollak

Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.

Language Modelling Text Classification

The NetViz terminology visualization tool and the use cases in karstology domain modeling

no code implementations LREC 2020 Senja Pollak, Vid Podpe{\v{c}}an, Dragana Miljkovic, Uro{\v{s}} Stepi{\v{s}}nik, {\v{S}}pela Vintar

We showcase the usefulness of the tool on examples from the karstology domain, where in the first use case we visualize the domain knowledge as represented in a manually annotated corpus of domain definitions, while in the second use case we show the power of visualization for domain understanding by visualizing automatically extracted knowledge in the form of triplets extracted from the karstology domain corpus.

TNT-KID: Transformer-based Neural Tagger for Keyword Identification

1 code implementation20 Mar 2020 Matej Martinc, Blaž Škrlj, Senja Pollak

With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity.

Keyword Extraction Language Modelling

Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift

no code implementations LREC 2020 Matej Martinc, Petra Kralj Novak, Senja Pollak

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.

Domain Adaptation

Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods

no code implementations28 Aug 2019 Fasih Haider, Senja Pollak, Pierre Albert, Saturnino Luz

A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology.

Emotion Recognition Feature Selection

Language comparison via network topology

1 code implementation16 Jul 2019 Blaž Škrlj, Senja Pollak

In our experiments, we employ eight different network topology metrics, and empirically showcase on a parallel corpus, how the methods can be used for modeling the relations between nine selected languages.

RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation

1 code implementation15 Jul 2019 Blaž Škrlj, Andraž Repar, Senja Pollak

Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems.

Keyword Extraction

Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style

no code implementations WS 2017 Ben Verhoeven, Iza {\v{S}}krjanec, Senja Pollak

Inspired by the TwiSty corpus and experiments (Verhoeven et al., 2016), we employed the Janes corpus (Erjavec et al., 2016) and its gender annotations to perform gender classification experiments on Twitter text comparing a token-based and a lemma-based approach.

General Classification Lemmatization

Irregularity Detection in Categorized Document Corpora

no code implementations LREC 2012 Borut Sluban, Senja Pollak, Roel Coesemans, Nada Lavra{\v{c}}

The paper presents an approach to extract irregularities in document corpora, where the documents originate from different sources and the analyst's interest is to find documents which are atypical for the given source.

Document Classification Outlier Detection +1

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