| 1. |
- Boman, Magnus, et al.
(författare)
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Learning Machines
- 2018
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Ingår i: <em>Learning, Inference and Control of Multi-Agent Systems</em>. ; , s. 610-613
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Konferensbidrag (refereegranskat)
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| 2. |
- Boman, Magnus, et al.
(författare)
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Learning machines in Internet-delivered psychological treatment
- 2019
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Ingår i: Progress in Artificial Intelligence. - : Springer Verlag. - 2192-6352 .- 2192-6360. ; 8:4, s. 475-485
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Tidskriftsartikel (refereegranskat)abstract
- A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.
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| 3. |
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| 4. |
- Ghoorchian, Kambiz, 1981-
(författare)
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Graph Algorithms for Large-Scale and Dynamic Natural Language Processing
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In Natural Language Processing, researchers design and develop algorithms to enable machines to understand and analyze human language. These algorithms benefit multiple downstream applications including sentiment analysis, automatic translation, automatic question answering, and text summarization. Topic modeling is one such algorithm that solves the problem of categorizing documents into multiple groups with the goal of maximizing the intra-group document similarity. However, the manifestation of short texts like tweets, snippets, comments, and forum posts as the dominant source of text in our daily interactions and communications, as well as being the main medium for news reporting and dissemination, increases the complexity of the problem due to scalability, sparsity, and dynamicity. Scalability refers to the volume of the messages being generated, sparsity is related to the length of the messages, and dynamicity is associated with the ratio of changes in the content and topical structure of the messages (e.g., the emergence of new phrases). We improve the scalability and accuracy of Natural Language Processing algorithms from three perspectives, by leveraging on innovative graph modeling and graph partitioning algorithms, incremental dimensionality reduction techniques, and rich language modeling methods. We begin by presenting a solution for multiple disambiguation on short messages, as opposed to traditional single disambiguation. The solution proposes a simple graph representation model to present topical structures in the form of dense partitions in that graph and applies disambiguation by extracting those topical structures using an innovative distributed graph partitioning algorithm. Next, we develop a scalable topic modeling algorithm using a novel dense graph representation and an efficient graph partitioning algorithm. Then, we analyze the effect of temporal dimension to understand the dynamicity in online social networks and present a solution for geo-localization of users in Twitter using a hierarchical model that combines partitioning of the underlying social network graph with temporal categorization of the tweets. The results show the effect of temporal dynamicity on users’ spatial behavior. This result leads to design and development of a dynamic topic modeling solution, involving an online graph partitioning algorithm and a significantly stronger language modeling approach based on the skip-gram technique. The algorithm shows strong improvement on scalability and accuracy compared to the state-of-the-art models. Finally, we describe a dynamic graph-based representation learning algorithm that modifies the partitioning algorithm to develop a generalization of our previous work. A strong representation learning algorithm is proposed that can be used for extracting high quality distributed and continuous representations out of any sequential data with local and hierarchical structural properties similar to natural language text.
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| 5. |
- Gogoulou, Evangelia, et al.
(författare)
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Predicting treatment outcome from patient texts : The case of internet-based cognitive behavioural therapy
- 2021
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Ingår i: EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. - : Association for Computational Linguistics (ACL). - 9781954085022 ; , s. 575-580
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Konferensbidrag (refereegranskat)abstract
- We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results. © 2021 Association for Computational Linguistics
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| 6. |
- Alkathiri, Abdul Aziz, et al.
(författare)
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Decentralized Word2Vec Using Gossip Learning
- 2021
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Ingår i: Proceedings of the 23<sup>rd</sup> Nordic Conference on Computational Linguistics (NoDaLiDa 2021).
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Konferensbidrag (refereegranskat)abstract
- Advanced NLP models require huge amounts of data from various domains to produce high-quality representations. It is useful then for a few large public and private organizations to join their corpora during training. However, factors such as legislation and user emphasis on data privacy may prevent centralized orchestration and data sharing among these organizations. Therefore, for this specific scenario, we investigate how gossip learning, a massively-parallel, data-private, decentralized protocol, compares to a shared-dataset solution. We find that the application of Word2Vec in a gossip learning framework is viable. Without any tuning, the results are comparable to a traditional centralized setting, with a reduction in ground-truth similarity scores as low as 4.3%. Furthermore, the results are up to 54.8% better than independent local training.
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| 7. |
- Argaw, Atelach Alemu, et al.
(författare)
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Dictionary-based Amharic-French information retrieval
- 2006
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Ingår i: Accessing Multilingual Information Repositories. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 354045697X ; , s. 83-92, s. 83-92
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Konferensbidrag (refereegranskat)abstract
- We present four approaches to the Amharic - French bilingual track at CLEF 2005. All experiments use a dictionary based approach to translate the Amharic queries into French Bags-of-words, but while one approach uses word sense discrimination on the translated side of the queries, the other one includes all senses of a translated word in the query for searching. We used two search engines: The SICS experimental engine and Lucene, hence four runs with the two approaches. Non-content bearing words were removed both before and after the dictionary lookup. TF/IDF values supplemented by a heuristic function was used to remove the stop words from the Amharic queries and two French stopwords lists were used to remove them from the French translations. In our experiments, we found that the SICS search engine performs better than Lucene and that using the word sense discriminated keywords produce a slightly better result than the full set of non discriminated keywords.
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| 9. |
- Axelsson, Sofia, 1987, et al.
(författare)
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Expressing Happiness in Different Languages
- 2016
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Ingår i: EUENGAGE Political Text Analysis Workshop, 21 – 22 June 2016, Amsterdam, Netherlands.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 10. |
- Berdicevskis, Aleksandrs, 1983, et al.
(författare)
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Superlim: A Swedish Language Understanding Evaluation Benchmark
- 2023
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Ingår i: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, December 6-10, 2023, Singapore / Houda Bouamor, Juan Pino, Kalika Bali (Editors). - Stroudsburg, PA : Association for Computational Linguistics. - 9798891760608
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Konferensbidrag (refereegranskat)
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