Results 31 to 40 of about 2,664 (259)
Real-World PP Attachment Disambiguation Dataset
, 2023 This resource contains a German dataset for real-world PP attachment disambiguation. The creation, analysis and experiment results of the dataset are described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited"
Rehbein, Ines, Do, Bich-Ngoc
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Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation
Journal of Integrative Bioinformatics, 2017 Word sense disambiguation (WSD) is an important step in biomedical text mining, which is responsible for assigning an unequivocal concept to an ambiguous term, improving the accuracy of biomedical information extraction systems.
Antunes Rui, Matos Sérgio
doaj +1 more source
PWNC: A Large-Scale Persian Corpus for Joint WSD and NER Using Semi-Supervised and Supervised Learning [PDF]
Journal of Artificial Intelligence and Data MiningWord Sense Disambiguation (WSD) is a longstanding challenge in natural language processing, particularly in morphologically rich and low-resource languages such as Persian.
Arash Keshtkar +2 more
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Word Sense Disambiguation Using Heterogeneous Language Resources [PDF]
, 2005 This paper proposes a robust method for word sense disambiguation (WSD) of Japanese. Four classifiers were combined in order to improve recall and applicability: one used example sentences in a machine readable dictionary (MRD), one used grammatical information in an MRD, and two classifiers were obtained by supervised learning from a sense-tagged ...
Kiyoaki Shirai, Takayuki Tamagaki
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Tool for Extracting PP Attachment Disambiguation Dataset
, 2023 This resource contains code to extract a PP attachment disambiguation dataset as described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". The input is in CoNLL format, and the output format is similar to the one
Rehbein, Ines, Do, Bich-Ngoc
core +1 more source
IEEE Access, 2020 Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in an input-text sequence to their correct references in a knowledge graph.
Zizheng Ji +3 more
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Tree-KGQA: An Unsupervised Approach for Question Answering Over Knowledge Graphs
IEEE Access, 2022 Most Knowledge Graph-based Question Answering (KGQA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive.
Md Rashad Al Hasan Rony +3 more
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An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
CoRR, 2018 In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word. Watasense has two modes of operation. The sparse mode uses
Ustalov, Dmitry +5 more
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Biology, 2022 The increasing incorporation of omics technologies into biomedical research and translational medicine presents challenges to end users of the large and complex datasets that are generated by these methods.
Joshua D. Breidenbach +3 more
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An Optimized Lesk-Based Algorithm for Word Sense Disambiguation
Open Computer Science, 2016 Computational complexity is a characteristic of almost all Lesk-based algorithms for word sense disambiguation (WSD). In this paper, we address this issue by developing a simple and optimized variant of the algorithm using topic composition in documents ...
Ayetiran Eniafe Festus, Agbele Kehinde
doaj +1 more source