2022
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Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings
Peng Liu
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Cristina Marco
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Jon Atle Gulla
Proceedings of the Thirteenth Language Resources and Evaluation Conference
This paper presents a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages: Danish, Norwegian and Swedish. This method benefits from the English Sentiwordnet and a thesaurus in one of the target languages. Sentiment information from the English resource is mapped to the target languages by using machine translation and similarity measures based on sentence embeddings. A number of experiments with Scandinavian languages are performed in order to determine the best working sentence embedding algorithm for this task. A careful extrinsic evaluation on several datasets yields state-of-the-art results using a simple rule-based sentiment analysis algorithm. The resources are made freely available under an MIT License.
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Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation
Yujie Xing
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Jinglun Cai
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Nils Barlaug
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Peng Liu
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Jon Atle Gulla
Findings of the Association for Computational Linguistics: NAACL 2022
Open-domain conversational systems are assumed to generate equally good responses on multiple domains. Previous work achieved good performance on the single corpus, but training and evaluating on multiple corpora from different domains are less studied. This paper explores methods of generating relevant responses for each of multiple multi-domain corpora. We first examine interleaved learning which intermingles multiple corpora as the baseline. We then investigate two multi-domain learning methods, labeled learning and multi-task labeled learning, which encode each corpus through a unique corpus embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word for a specific corpus compared to other corpora. Based on DF, we propose weighted learning, a method that integrates DF to the loss function. We also adopt DF as a new evaluation metric. Extensive experiments show that our methods gain significant improvements on both automatic and human evaluation. We share our code and data for reproducibility.
2016
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Political News Sentiment Analysis for Under-resourced Languages
Patrik F. Bakken
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Terje A. Bratlie
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Cristina Marco
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Jon Atle Gulla
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
This paper presents classification results for the analysis of sentiment in political news articles. The domain of political news is particularly challenging, as journalists are presumably objective, whilst at the same time opinions can be subtly expressed. To deal with this challenge, in this work we conduct a two-step classification model, distinguishing first subjective and second positive and negative sentiment texts. More specifically, we propose a shallow machine learning approach where only minimal features are needed to train the classifier, including sentiment-bearing Co-Occurring Terms (COTs) and negation words. This approach yields close to state-of-the-art results. Contrary to results in other domains, the use of negations as features does not have a positive impact in the evaluation results. This method is particularly suited for languages that suffer from a lack of resources, such as sentiment lexicons or parsers, and for those systems that need to function in real-time.
2011
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Enhancing the HL-SOT Approach to Sentiment Analysis via a Localized Feature Selection Framework
Wei Wei
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Jon Atle Gulla
Proceedings of 5th International Joint Conference on Natural Language Processing
2010
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Sentiment Learning on Product Reviews via Sentiment Ontology Tree
Wei Wei
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Jon Atle Gulla
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
1996
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A Sign Expansion Approach to Dynamic, Multi-Purpose Lexicons
Jon Atle Gulla
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Sjur Nørstebø Moshagen
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics