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Found 33 papers, 21 papers with code
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.
Negation in Norwegian: an annotated dataset
This paper introduces NorecNeg – the first annotated dataset of negation for Norwegian.
XNLIeu: a dataset for cross-lingual NLI in Basque
We have conducted a series of experiments using mono- and multilingual LLMs to assess a) the effect of professional post-edition on the MT system; b) the best cross-lingual strategy for NLI in Basque; and c) whether the choice of the best cross-lingual strategy is influenced by the fact that the dataset is built by translation.
Natural Language Inference
Natural Language Understanding
+1
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
This is particularly of interest when we have access to a multilingual large language model, because we could request labels with English prompts even for non-English data.
Annotating Norwegian Language Varieties on Twitter for Part-of-Speech
We show that models trained on Universal Dependency (UD) data perform worse when evaluated against this dataset, and that models trained on Bokm{\aa}l generally perform better than those trained on Nynorsk.
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.
Evaluating morphological typology in zero-shot cross-lingual transfer
Furthermore, POS tagging is more sensitive to morphological typology than sentiment analysis and, on this task, models perform much better on fusional languages than on the other typologies.
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.
The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus
However, the interplay between language similarity and difference in script on cross-lingual transfer is a less studied problem.
skweak: Weak Supervision Made Easy for NLP
skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling.
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.
NorDial: A Preliminary Corpus of Written Norwegian Dialect Use
Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere.
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.
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results.
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain.
Cross-lingual Emotion Intensity Prediction
Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets.
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.
Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study
As expected, the choice of annotated source language for projection to a target leads to better results for source-target language pairs which are similar.
LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish
This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign.
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.
On the Effect of Word Order on Cross-lingual Sentiment Analysis
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional networks (CNNs).
Cross-Lingual Sentiment Classification
General Classification
+4
Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis
This paper describes T{\"u}bingen-Oslo team{'}s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign.
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains
Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models.
Cross-Lingual Sentiment Classification
Machine Translation
+5
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification
While sentiment analysis has become an established field in the NLP community, research into languages other than English has been hindered by the lack of resources.
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes).
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification).
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.
Cross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language.
