Senja Pollak


2022

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E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression
Ilija Tavchioski | Boshko Koloski | Blaž Škrlj | Senja Pollak
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Depression is a mental illness that negatively affects a person’s well-being and can, if left untreated, lead to serious consequences such as suicide. Therefore, it is important to recognize the signs of depression early. In the last decade, social media has become one of the most common places to express one’s feelings. Hence, there is a possibility of text processing and applying machine learning techniques to detect possible signs of depression. In this paper, we present our approaches to solving the shared task titled Detecting Signs of Depression from Social Media Text. We explore three different approaches to solve the challenge: fine-tuning BERT model, leveraging AutoML for the construction of features and classifier selection and finally, we explore latent spaces derived from the combination of textual and knowledge-based representations. We ranked 9th out of 31 teams in the competition. Our best solution, based on knowledge graph and textual representations, was 4.9% behind the best model in terms of Macro F1, and only 1.9% behind in terms of Recall.

2021

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Supervised and Unsupervised Neural Approaches to Text Readability
Matej Martinc | Senja Pollak | Marko Robnik-Šikonja
Computational Linguistics, Volume 47, Issue 1 - March 2021

Abstract We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.

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Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing
Bogdan Babych | Olga Kanishcheva | Preslav Nakov | Jakub Piskorski | Lidia Pivovarova | Vasyl Starko | Josef Steinberger | Roman Yangarber | Michał Marcińczuk | Senja Pollak | Pavel Přibáň | Marko Robnik-Šikonja
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

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Exploratory Analysis of News Sentiment Using Subgroup Discovery
Anita Valmarska | Luis Adrián Cabrera-Diego | Elvys Linhares Pontes | Senja Pollak
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

In this study, we present an exploratory analysis of a Slovenian news corpus, in which we investigate the association between named entities and sentiment in the news. We propose a methodology that combines Named Entity Recognition and Subgroup Discovery - a descriptive rule learning technique for identifying groups of examples that share the same class label (sentiment) and pattern (features - Named Entities). The approach is used to induce the positive and negative sentiment class rules that reveal interesting patterns related to different Slovenian and international politicians, organizations, and locations.

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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Jakub Piskorski | Bogdan Babych | Zara Kancheva | Olga Kanishcheva | Maria Lebedeva | Michał Marcińczuk | Preslav Nakov | Petya Osenova | Lidia Pivovarova | Senja Pollak | Pavel Přibáň | Ivaylo Radev | Marko Robnik-Sikonja | Vasyl Starko | Josef Steinberger | Roman Yangarber
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2021 Conference. Ten teams participated in the competition. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task. Detailed valuation information is available on the shared task web page.

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BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers
Enja Kokalj | Blaž Škrlj | Nada Lavrač | Senja Pollak | Marko Robnik-Šikonja
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Transformer-based neural networks offer very good classification performance across a wide range of domains, but do not provide explanations of their predictions. While several explanation methods, including SHAP, address the problem of interpreting deep learning models, they are not adapted to operate on state-of-the-art transformer-based neural networks such as BERT. Another shortcoming of these methods is that their visualization of explanations in the form of lists of most relevant words does not take into account the sequential and structurally dependent nature of text. This paper proposes the TransSHAP method that adapts SHAP to transformer models including BERT-based text classifiers. It advances SHAP visualizations by showing explanations in a sequential manner, assessed by human evaluators as competitive to state-of-the-art solutions.

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Extending Neural Keyword Extraction with TF-IDF tagset matching
Boshko Koloski | Senja Pollak | Blaž Škrlj | Matej Martinc
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

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. In this work, we develop and evaluate our methods on four novel data sets covering less-represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian, and Russian). First, we perform evaluation of two supervised neural transformer-based methods, Transformer-based Neural Tagger for Keyword Identification (TNT-KID) and Bidirectional Encoder Representations from Transformers (BERT) with an additional Bidirectional Long Short-Term Memory Conditional Random Fields (BiLSTM CRF) classification head, and compare them to a baseline Term Frequency - Inverse Document Frequency (TF-IDF) based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer-based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate for usage as a recommendation system in the media house environment.

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Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection
Andraž Pelicon | Ravi Shekhar | Matej Martinc | Blaž Škrlj | Matthew Purver | Senja Pollak
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

We present a system for zero-shot cross-lingual offensive language and hate speech classification. The system was trained on English datasets and tested on a task of detecting hate speech and offensive social media content in a number of languages without any additional training. Experiments show an impressive ability of both models to generalize from English to other languages. There is however an expected gap in performance between the tested cross-lingual models and the monolingual models. The best performing model (offensive content classifier) is available online as a REST API.

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Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces
Blaž Škrlj | Shane Sheehan | Nika Eržen | Marko Robnik-Šikonja | Saturnino Luz | Senja Pollak
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation. Commonly comprising hundreds of millions of parameters, these models offer state-of-the-art performance, but at the expense of interpretability. The attention mechanism is the main component of transformer networks. We present AttViz, a method for exploration of self-attention in transformer networks, which can help in explanation and debugging of the trained models by showing associations between text tokens in an input sequence. We show that existing deep learning pipelines can be explored with AttViz, which offers novel visualizations of the attention heads and their aggregations. We implemented the proposed methods in an online toolkit and an offline library. Using examples from news analysis, we demonstrate how AttViz can be used to inspect and potentially better understand what a model has learned.

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EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
Senja Pollak | Marko Robnik-Šikonja | Matthew Purver | Michele Boggia | Ravi Shekhar | Marko Pranjić | Salla Salmela | Ivar Krustok | Tarmo Paju | Carl-Gustav Linden | Leo Leppänen | Elaine Zosa | Matej Ulčar | Linda Freienthal | Silver Traat | Luis Adrián Cabrera-Diego | Matej Martinc | Nada Lavrač | Blaž Škrlj | Martin Žnidaršič | Andraž Pelicon | Boshko Koloski | Vid Podpečan | Janez Kranjc | Shane Sheehan | Emanuela Boros | Jose G. Moreno | Antoine Doucet | Hannu Toivonen
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

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. The collected resources were offered to participants of a hackathon organized as part of the EACL Hackashop on News Media Content Analysis and Automated Report Generation in February 2021. The hackathon had six participating teams who addressed different challenges, either from the list of proposed challenges or their own news-industry-related tasks. This paper goes beyond the scope of the hackathon, as it brings together in a coherent and compact form most of the resources developed, collected and released by the EMBEDDIA project. Moreover, it constitutes a handy source for news media industry and researchers in the fields of Natural Language Processing and Social Science.

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Interesting cross-border news discovery using cross-lingual article linking and document similarity
Boshko Koloski | Elaine Zosa | Timen Stepišnik-Perdih | Blaž Škrlj | Tarmo Paju | Senja Pollak
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Team Name: team-8 Embeddia Tool: Cross-Lingual Document Retrieval Zosa et al. Dataset: Estonian and Latvian news datasets abstract: Contemporary news media face increasing amounts of available data that can be of use when prioritizing, selecting and discovering new news. In this work we propose a methodology for retrieving interesting articles in a cross-border news discovery setting. More specifically, we explore how a set of seed documents in Estonian can be projected in Latvian document space and serve as a basis for discovery of novel interesting pieces of Latvian news that would interest Estonian readers. The proposed methodology was evaluated by Estonian journalist who confirmed that in the best setting, from top 10 retrieved Latvian documents, half of them represent news that are potentially interesting to be taken by the Estonian media house and presented to Estonian readers.

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EMBEDDIA hackathon report: Automatic sentiment and viewpoint analysis of Slovenian news corpus on the topic of LGBTIQ+
Matej Martinc | Nina Perger | Andraž Pelicon | Matej Ulčar | Andreja Vezovnik | Senja Pollak
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

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. The focus is on the differences in reporting between quality news media with long tradition and news media with financial and political connections to SDS, a Slovene right-wing political party. The results suggest that political affiliation of the media can affect the sentiment distribution of articles and the framing of specific LGBTIQ+ specific topics, such as same-sex marriage.

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Preliminary experimentation with combinations and extensions of forward-looking sentence detection wordlists
Jan Štihec | Senja Pollak | Martin Žnidaršič
Proceedings of the 3rd Financial Narrative Processing Workshop

2020

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The NetViz terminology visualization tool and the use cases in karstology domain modeling
Senja Pollak | Vid Podpečan | Dragana Miljkovic | Uroš Stepišnik | Špela Vintar
Proceedings of the 6th International Workshop on Computational Terminology

We present the NetViz terminology visualization tool and apply it to the domain modeling of karstology, a subfield of geography studying karst phenomena. The developed tool allows for high-performance online network visualization where the user can upload the terminological data in a simple CSV format, define the nodes (terms, categories), edges (relations) and their properties (by assigning different node colors), and then edit and interactively explore domain knowledge in the form of a network. 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. The application is entirely web-based without any need for downloading or special configuration. The source code of the web application is also available under the permissive MIT license, allowing future extensions for developing new terminological applications.

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SemEval-2020 Task 3: Graded Word Similarity in Context
Carlos Santos Armendariz | Matthew Purver | Senja Pollak | Nikola Ljubešić | Matej Ulčar | Ivan Vulić | Mohammad Taher Pilehvar
Proceedings of the Fourteenth Workshop on Semantic Evaluation

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. We received 15 submissions and 11 system description papers. A new dataset (CoSimLex) was created for evaluation in this task: it contains pairs of words, each annotated within two different contexts. Systems beat the baselines by significant margins, but few did well in more than one language or subtask. Almost every system employed a Transformer model, but with many variations in the details: WordNet sense embeddings, translation of contexts, TF-IDF weightings, and the automatic creation of datasets for fine-tuning were all used to good effect.

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Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift
Matej Martinc | Petra Kralj Novak | Senja Pollak
Proceedings of the 12th Language Resources and Evaluation Conference

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. The results of our experiments in the domain specific LiverpoolFC corpus suggest that the proposed method has performance comparable to the current state-of-the-art without requiring any time consuming domain adaptation on large corpora. The results on the newly created Brexit news corpus suggest that the method can be successfully used for the detection of a short-term yearly semantic shift. And lastly, the model also shows promising results in a multilingual settings, where the task was to detect differences and similarities between diachronic semantic shifts in different languages.

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CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
Carlos Santos Armendariz | Matthew Purver | Matej Ulčar | Senja Pollak | Nikola Ljubešić | Mark Granroth-Wilding
Proceedings of the 12th Language Resources and Evaluation Conference

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far.

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Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings
Špela Vintar | Larisa Grčić Simeunović | Matej Martinc | Senja Pollak | Uroš Stepišnik
Proceedings of the 13th Workshop on Building and Using Comparable Corpora

We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings. In a multilingual frame-based domain model, specific features of a concept are typically described through a set of non-arbitrary semantic relations. In karstology, our domain of choice which we are exploring though a comparable corpus in English and Croatian, karst phenomena such as landforms are usually described through their FORM, LOCATION, CAUSE, FUNCTION and COMPOSITION. We propose an approach to mine words pertaining to each of these relations by using a small number of seed adjectives, for which we retrieve closest words using word embeddings and then use intersections of these neighbourhoods to refine our search. Such cross-language expansion of semantically-rich vocabulary is a valuable aid in improving the coverage of a multilingual knowledge base, but also in exploring differences between languages in their respective conceptualisations of the domain.

2018

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Reusable workflows for gender prediction
Matej Martinc | Senja Pollak
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style
Ben Verhoeven | Iza Škrjanec | Senja Pollak
Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

We present results of the first gender classification experiments on Slovene text to our knowledge. 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. We find that the token-based approach (92.6% accuracy), containing gender markings related to the author, outperforms the lemma-based approach by about 5%. Especially in the lemmatized version, we also observe stylistic and content-based differences in writing between men (e.g. more profane language, numerals and beer mentions) and women (e.g. more pronouns, emoticons and character flooding). Many of our findings corroborate previous research on other languages.

2015

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Predicting the Level of Text Standardness in User-generated Content
Nikola Ljubešić | Darja Fišer | Tomaž Erjavec | Jaka Čibej | Dafne Marko | Senja Pollak | Iza Škrjanec
Proceedings of the International Conference Recent Advances in Natural Language Processing

2012

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Irregularity Detection in Categorized Document Corpora
Borut Sluban | Senja Pollak | Roel Coesemans | Nada Lavrač
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

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. The main contribution of the paper is a voting-based approach to irregularity detection and its evaluation on a collection of newspaper articles from two sources: Western (UK and US) and local (Kenyan) media. The evaluation of a domain expert proves that the method is very effective in uncovering interesting irregularities in categorized document corpora.

2011

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Building and Using Comparable Corpora for Domain-Specific Bilingual Lexicon Extraction
Darja Fišer | Nikola Ljubešić | Špela Vintar | Senja Pollak
Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web

2010

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Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources
Darja Fišer | Senja Pollak | Špela Vintar
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The paper presents an innovative approach to extract Slovene definition candidates from domain-specific corpora using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. First, a classification model was trained on examples from Slovene Wikipedia which was then used to find well-formed definitions among the extracted candidates. The results of the experiment are encouraging, with accuracy ranging from 67% to 71%. The paper also addresses some drawbacks of the approach and suggests ways to overcome them in future work.