This thesis introduces an innovative methodology of combining some traditional dictionary based approaches to word sense disambiguation (semantic similarity measures and overlap of word glosses, both based on WordNet) with some graph-based centrality methods, namely the degree of the vertices, Pagerank, closeness, and betweenness. The approach is completely unsupervised, and is based on creating graphs for the words to be disambiguated. We experiment with several possible combinations of the semantic similarity measures as the first stage in our experiments. The next stage attempts to score individual vertices in the graphs previously created based on several graph connectivity measures. During the final stage, several voting schemes are applied on the results obtained from the different centrality algorithms. The most important contributions of this work are not only that it is a novel approach and it works well, but also that it has great potential in overcoming the new-knowledge-acquisition bottleneck which has apparently brought research in supervised WSD as an explicit application to a plateau. The type of research reported in this thesis, which does not require manually annotated data, holds promise of a lot of new and interesting things, and our work is one of the first steps, despite being a small one, in this direction. The complete system is built and tested on standard benchmarks, and is comparable with work done on graph-based word sense disambiguation as well as lexical chains. The evaluation indicates that the right combination of the above mentioned metrics can be used to develop an unsupervised disambiguation engine as powerful as the state-of-the-art in WSD.

This Master's Thesis prepares Evaluation Set for Problems of Recognition and Disambiguation of Named Entities. Evaluation Set is created using Automatization and Crowdsourcing. Evaluation Set can be used in testing Edge Cases in Recognition and Disambiguation of Named Entities.

Tato práce se zabývá desambiguací autorů v databázích odborné literatury. Z důvodu nejednoznačnosti jmen autorů se v těchto databázích vyskytují problémy s přisuzováním autorství publikací a tím spojenou analýzou citací, vlivu autorů apod.  Tato práce se zabývá otázkou odhadu správného počtu autorů a zkoumá možnosti použití již existujících služeb. Součástí této práce je návrh metody pro desambiguaci autorů. Tato metoda byla implementována a evaluována v rámci systému CORE.

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京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第17928号 / 情博第510号 / 新制||情||90(附属図書館) / 30748 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 奥乃 博, 教授 河原 達也, 教授 山本 章博 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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Text-To-Speech systems are challenged by the presence of homographs, words that have more than one possible pronunciation. Rule-based approaches are often still the preferred solution to this issue in the industry. However, there have been multiple attempts to solve the ‘homograph issue’, by exploring statistical-based, neural-based, and hybrid techniques, mostly for English. Ploujnikov and Ravanelli (2022) proposed a neural-based grapheme-to-phoneme framework, SoundChoice, which comes as an RNN and a transformer version and can be fine-tuned for homograph disambiguation thanks to a weighted homograph loss. This thesis trains and tests this framework on Italian, instead of English, to see how it performs on a different language. Moreover, seeing as the available data containing homographs was insufficient for this task, the thesis experiments using ChatGPT as a data-generating tool. SoundChoice was also investigated for out-of-domain evaluation by testing it on data from a Corpus. The results showed that the RNN model reached a 71% accuracy from a baseline of 59%. A better performance was observed for the transformers model which went from 57% to 74%. Further analysis would be needed to draw more solid conclusions as to the origin of this gap and the models should be trained on Corpus data and tested on ChatGPT data to assess whether ChatGPT-generated data is, indeed, suitable as a replacement for Corpus data.

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