141 citations · 180 across the 15 of their papers we have counts for
19 papers
A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models
Huiyao Chen, Meishan Zhang, Jing Li +4
Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despit…
Entity-Level Sentiment: More than the Sum of Its Parts
Egil Rønningstad, Roman Klinger, Lilja Øvrelid +1
In sentiment analysis of longer texts, there may be a variety of topics discussed, of entities mentioned, and of sentiments expressed regarding each entity. We find a lack of studi…
Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis
Anthi Papadopoulou, Pierre Lison, Mark Anderson +2
Text sanitization is the task of redacting a document to mask all occurrences of (direct or indirect) personal identifiers, with the goal of concealing the identity of the individu…
Emotion Analysis of Tweets Banning Education in Afghanistan
Mohammad Ali Hussiny, Lilja Øvrelid
This paper introduces the first emotion annotated dataset for the Dari variant of Persian spoken in Afghanistan. The LetHerLearn dataset contains 7,600 tweets posted in reaction to…
Tokenization with Factorized Subword Encoding
David Samuel, Lilja Øvrelid
In recent years, language models have become increasingly larger and more complex. However, the input representations for these models continue to rely on simple and greedy subword…
Entity-Level Sentiment Analysis (ELSA): An exploratory task survey
Egil Rønningstad, Erik Velldal, Lilja Øvrelid
This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document -- what we refer to as Entity-Leve…
Trained on 100 million words and still in shape: BERT meets British National Corpus
David Samuel, Andrey Kutuzov, Lilja Øvrelid +1
While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-bala…
Contextualized language models for semantic change detection: lessons learned
Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
We present a qualitative analysis of the (potentially erroneous) outputs of contextualized embedding-based methods for detecting diachronic semantic change. First, we introduce an…
Building a Norwegian Lexical Resource for Medical Entity Recognition
Ildikó Pilán, Pål H. Brekke, Lilja Øvrelid
We present a large Norwegian lexical resource of categorized medical terms. The resource merges information from large medical databases, and contains over 77,000 unique entries, i…
One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem a…
Sentiment analysis is not solved! Assessing and probing sentiment classification
Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Neural methods for SA have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative diff…
Probing Multilingual Sentence Representations With X-Probe
Vinit Ravishankar, Lilja Øvrelid, Erik Velldal
This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain. In doing so, we make two contributions: first, we provide datasets…
Improving Sentiment Analysis with Multi-task Learning of Negation
Jeremy Barnes, Erik Velldal, Lilja Øvrelid
Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most preva…
Diachronic word embeddings and semantic shifts: a survey
Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski +1
Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embeddin…
Syntactic Dependency Representations in Neural Relation Classification
Farhad Nooralahzadeh, Lilja Øvrelid
We investigate the use of different syntactic dependency representations in a neural relation classification task and compare the CoNLL, Stanford Basic and Universal Dependencies s…
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
Farhad Nooralahzadeh, Lilja Øvrelid, Jan Tore Lønning
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest d…
NoReC: The Norwegian Review Corpus
Erik Velldal, Lilja Øvrelid, Eivind Alexander Bergem +3
This paper presents the Norwegian Review Corpus (NoReC), created for training and evaluating models for document-level sentiment analysis. The full-text reviews have been collected…
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words. The set-up is similar to the well-known analogies tas…
Redefining part-of-speech classes with distributional semantic models
Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
This paper studies how word embeddings trained on the British National Corpus interact with part of speech boundaries. Our work targets the Universal PoS tag set, which is currentl…