Results 121 to 130 of about 58,551 (288)
MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation
International Joint Conference on Artificial Intelligence, 2020 The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation.
Edoardo Barba +4 more
semanticscholar +1 more source
Word Sense Disambiguation and Information Retrieval [PDF]
, 1994 Starting with a review of previous research that attempted to improve the representation of documents in IR systems, this research is reassessed in the light of word sense ambiguity. It will be shown that a number of the attempts' successes or failures were due to the noticing or ignoring of ambiguity.
openaire +2 more sources
Language Learning, EarlyView.Abstract We investigate what is learned from exposure to usage in verbal morphology using an error correction mechanism within an associative learning framework. We computationally simulated how second language (L2) learners would respond to naturalistic input of aspectual usage, characterized by “imperfect contingencies,” given two types of ...
Justyna Mackiewicz +2 more
wiley +1 more source
Journal of Language ModellingWe develop a three-part approach to Verb Sense Disambiguation (VSD) in German. After considering a set of lexical resources and corpora, we arrive at a statistically motivated selection of a subset of verbs and their senses from GermaNet.
Dominik Mattern +3 more
doaj +2 more sources
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
Toward Universal Word Sense Disambiguation Using Deep Neural Networks
IEEE Access, 2019 Traditionally, approaches based on neural networks to solve the problem of disambiguation of the meaning of words (WSD) use a set of classifiers at the end, which results in a specialization in a single set of words-those for which they were trained ...
Hiram Calvo +3 more
doaj +1 more source
Hedgehog Pillows and Squirrel Plates: Priming Semantic Structure in Children's Comprehension
Language Learning, EarlyView.Abstract We report three expression–picture‐matching experiments targeting preschoolers’ semantic processing. We assessed whether 3‐ and 4‐year‐olds’ interpretations of ambiguous novel noun–noun combinations (e.g., hedgehog pillow) were affected by immediate language experience and what role lexical items played in this process.
Judit Fazekas +2 more
wiley +1 more source
Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings
Conference on Machine Translation, 2017 Word sense disambiguation is necessary in translation because different word senses often have different translations. Neural machine translation models learn different senses of words as part of an end-to-end translation task, and their capability to ...
Annette Rios Gonzales +2 more
semanticscholar +1 more source
The Problem of Christ’s Acquired Knowledge
Modern Theology, EarlyView.Abstract Thomas Aquinas is universally applauded for his “courage and perspicacity” in eventually admitting an acquired knowledge in Christ. According to this doctrine, Christ, through the experience of his senses, came to know what he previously did not know.
Joshua H. Lim
wiley +1 more source
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations
Conference on Empirical Methods in Natural Language Processing, 2019 Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed.
Marco Maru +3 more
semanticscholar +1 more source