Results 101 to 110 of about 83,408 (318)
CSI: A Coarse Sense Inventory for 85% Word Sense Disambiguation
AAAI Conference on Artificial Intelligence, 2020 Word Sense Disambiguation (WSD) is the task of associating a word in context with one of its meanings. While many works in the past have focused on raising the state of the art, none has even come close to achieving an F-score in the 80% ballpark when ...
Caterina Lacerra +3 more
semanticscholar +1 more source
Exploring a Systems Engineering Approach to Modelling Human Communication
Systems Research and Behavioral Science, EarlyView.ABSTRACT Communication is a crucial process for any successful purposeful human activity system as it mediates the information‐based relationships between a system's elements and parts, purpose(s) and boundaries. Human communication is more than just a process; it is a subsystem that interacts with other human activity systems, parts, elements and ...
Ryan Hekker +2 more
wiley +1 more source
Systems Engineering, EarlyView.ABSTRACT Multi‐purpose large language models (LLMs), a subset of generative artificial intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep‐domain knowledge and ...
Taylan G. Topcu +3 more
wiley +1 more source
Word Sense Disambiguation Based on RegNet With Efficient Channel Attention and Dilated Convolution
IEEE Access, 2023 Word sense disambiguation (WSD) is one of key problems in field of natural language processing. Ambiguous word often has different meanings in different contexts.
Chun-Xiang Zhang +2 more
doaj +1 more source
Journal of Research in Science Teaching, EarlyView.ABSTRACT Promoting argumentation based on evidence allows students to give meaning to the phenomena observed, enabling the construction of knowledge. Children's argumentative discourse can be activated by appropriate instruction, but there is little information on argumentation at early ages and there is a need for the teacher to understand how to ...
Lidia Caño, Josu Sanz
wiley +1 more source
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
TESOL Quarterly, EarlyView.Abstract Recent research on learner factors in task‐based language teaching (TBLT) has demonstrated positive effects for treatments that draw on learners' personal experiences. However, the specific processes responsible for these effects are not well understood.
Taghreed Qahl, Craig Lambert
wiley +1 more source
Word Sense Disambiguation: An Overview [PDF]
Language and Linguistics Compass, 2009 AbstractWord sense disambiguation is a subfield of computational linguistics in which computer systems are designed to determine the appropriate meaning of a word as it appears in the linguistic context. This article provides a survey of what has been done in this area: the ways that word meaning can be represented in the computer, the approaches taken
openaire +2 more sources
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation
Conference on Empirical Methods in Natural Language Processing, 2020 Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks.
Ming Wang, Yinglin Wang
semanticscholar +1 more source
Unified Neural Lexical Analysis Via Two‐Stage Span Tagging
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Lexical analysis is a fundamental task in natural language processing, which involves several subtasks, such as word segmentation (WS), part‐of‐speech (POS) tagging, and named entity recognition (NER). Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.
Yantuan Xian +5 more
wiley +1 more source