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Found 43 papers, 27 papers with code
NARC – Norwegian Anaphora Resolution Corpus
We present the Norwegian Anaphora Resolution Corpus (NARC), the first publicly available corpus annotated with anaphoric relations between noun phrases for Norwegian.
Classification of Syncope Cases in Norwegian Medical Records
Loss of consciousness, so-called syncope, is a commonly occurring symptom associated with worse prognosis for a number of heart-related diseases.
Occupational Biases in Norwegian and Multilingual Language Models
In this paper we explore how a demographic distribution of occupations, along gender dimensions, is reflected in pre-trained language models.
Using Gender- and Polarity-Informed Models to Investigate Bias
More specifically, we add information about the gender of critics and book authors when classifying the polarity of book reviews, and the polarity of the reviews when classifying the genders of authors and critics.
Negation in Norwegian: an annotated dataset
This paper introduces NorecNeg – the first annotated dataset of negation for Norwegian.
SemEval 2022 Task 10: Structured Sentiment Analysis
In this paper, we introduce the first SemEval shared task on Structured Sentiment Analysis, for which participants are required to predict all sentiment graphs in a text, where a single sentiment graph is composed of a sentiment holder, target, expression and polarity.
Lexicon information in neural sentiment analysis: a multi-task learning approach
This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models.
Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian
This paper documents the creation of a large-scale dataset of evaluative sentences – i. e. both subjective and objective sentences that are found to be sentiment-bearing – based on mixed-domain professional reviews from various news-sources.
Gender and sentiment, critics and authors: a dataset of Norwegian book reviews
We also explore the differences in how this is done by male and female critics.
Multilingual ELMo and the Effects of Corpus Sampling
Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages.
Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis
The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information.
Emotion Analysis of Tweets Banning Education in Afghanistan
This paper introduces the first emotion annotated dataset for the Dari variant of Persian spoken in Afghanistan.
JSEEGraph: Joint Structured Event Extraction as Graph Parsing
We propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing.
Tokenization with Factorized Subword Encoding
In recent years, language models have become increasingly larger and more complex.
Text-To-KG Alignment: Comparing Current Methods on Classification Tasks
In contrast to large text corpora, knowledge graphs (KG) provide dense and structured representations of factual information.
NorBench -- A Benchmark for Norwegian Language Models
We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics.
NorQuAD: Norwegian Question Answering Dataset
In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension.
Entity-Level Sentiment Analysis (ELSA): An exploratory task survey
We show that sentiment in our dataset is expressed not only with an entity mention as target, but also towards targets with a sentiment-relevant relation to a volitional entity.
Measuring Normative and Descriptive Biases in Language Models Using Census Data
We investigate in this paper how distributions of occupations with respect to gender is reflected in pre-trained language models.
Trained on 100 million words and still in shape: BERT meets British National Corpus
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-balanced, and publicly available English text source -- the British National Corpus.
EventGraph at CASE 2021 Task 1: A General Graph-based Approach to Protest Event Extraction
This paper presents our submission to the 2022 edition of the CASE 2021 shared task 1, subtask 4.
EventGraph: Event Extraction as Semantic Graph Parsing
Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; and 3) capturing the complicated interaction between event arguments and triggers.
Contextualized language models for semantic change detection: lessons learned
Our findings show that contextualized methods can often predict high change scores for words which are not undergoing any real diachronic semantic shift in the lexicographic sense of the term (or at least the status of these shifts is questionable).
Bootstrapping Text Anonymization Models with Distant Supervision
Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals.
Direct parsing to sentiment graphs
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text.
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods.
Structured Sentiment Analysis as Dependency Graph Parsing
Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e, g,, target extraction or targeted polarity classification.
Large-Scale Contextualised Language Modelling for Norwegian
We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training.
If you've got it, flaunt it: Making the most of fine-grained sentiment annotations
Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation.
Building a Norwegian Lexical Resource for Medical Entity Recognition
We present a large Norwegian lexical resource of categorized medical terms.
A Systematic Comparison of Architectures for Document-Level Sentiment Classification
Documents are composed of smaller pieces - paragraphs, sentences, and tokens - that have complex relationships between one another.
A Fine-Grained Sentiment Dataset for Norwegian
We introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion.
NorNE: Annotating Named Entities for Norwegian
This paper presents NorNE, a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank.
One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists.
Improving Sentiment Analysis with Multi-task Learning of Negation
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.
Sentiment analysis is not solved! Assessing and probing sentiment classification
Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.
Probing Multilingual Sentence Representations With X-Probe
This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain.
Diachronic word embeddings and semantic shifts: a survey
Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models.
Syntactic Dependency Representations in Neural Relation Classification
We investigate the use of different syntactic dependency representations in a neural relation classification task and compare the CoNLL, Stanford Basic and Universal Dependencies schemes.
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers.
NoReC: The Norwegian Review Corpus
As resources for sentiment analysis have so far been unavailable for Norwegian, NoReC represents a highly valuable and sought-after addition to Norwegian language technology.
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words.
