18 papers · ranked by Valyu relevance
Sina Ahmadi
Dictionaries are fundamental resources for people to learn and document languages as well as for computers to process natural languages. A dictionary provides a finegrained structure and description of the vocabulary of a language. With decades of advances in electronic lexicography, a significant amount of…
Masato Kikuchi, Masatsugu Ono, Toshioki Soga, Tetsu Tanabe + 1 more
Although WordNet is a valuable resource owing to its structured semantic networks and extensive vocabulary, its fine-grained sense distinctions can be challenging for second-language learners. To address this, we developed a WordNet annotated with the Common European Framework of Reference for Languages (CEFR)…
Lorenzo Massai
Keywords: Natural language processing Query reformulation Classification systems Query classification Semantic expansion Pseudo-relevance feedback Semantic search engines Intent estimation Ontology-based retrieval systems With the increasing demand of intelligent systems capable of operating in different contexts (e.g.…
Abed Alhakim Freihat, Hadi Khalilia, Gábor Bella, Fausto Giunchiglia
High-quality WordNets are crucial for achieving high-quality results in NLP applications that rely on such resources. However, the wordnets of most languages suffer from serious issues of correctness and completeness with respect to the words and word meanings they define, such as incorrect lemmas, missing glosses and…
Suzanne Stevenson, Paola Merlo
To process language in a way that is compatible with human expectations in a communicative interaction, we need computational representations of lexical properties that form the basis of human knowledge of words. In this article, we concentrate on word-level semantics. We discuss key concepts and issues that underlie…
Dan John Velasco, Axel Alba, Trisha Gail Pelagio, Bryce Anthony Ramirez + 4 more
'Bryce Anthony Ramirez' 'Jan Christian Blaise Cruz' 'Unisse Chua' 'Briane Paul V. Samson' 'Charibeth Cheng'] Wordnets are indispensable tools for various natural language processing applications. Unfortunately, wordnets get outdated, and producing or updating wordnets can be slow and costly in terms of time and…
Karol Marek Klimczak, Jan Makary Fryczak, Dominika Hadro, Justyna Fijałkowska
'Justyna Fijałkowska'] This paper presents a technique for sentiment measurement in many languages. The method allows researchers to efficiently analyze corporate documents, management reports, and financial statements using python. When the texts are written in many languages, the method extracts equivalent…
Leonardo Fernandino, Lisa L. Conant
The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that lexical semantic representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this…
Małgorzata Wierzba, Monika Riegel, Jan Kocoń, Piotr Miłkowski + 7 more
'Arkadiusz Janz' 'Katarzyna Klessa' 'Konrad Juszczyk' 'Barbara Konat' 'Damian Grimling' 'Maciej Piasecki' 'Artur Marchewka'] Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise…
Enock Niyonkuru, Mauricio Soto Gomez, Elena Casiraghi, Stephan Antogiovanni + 4 more
Concept embeddings are low-dimensional vector representations of concepts such as MeSH:D009203 (Myocardial Infarction), whose similarity in the embedded vector space reflects their semantic similarity. Here, we test the hypothesis that non-biomedical concept synonym replacement can improve the quality of biomedical…
Mingxue Fu, Guoqiu Chen, Yijie Zhang, Mingzhe Zhang + 1 more
A central challenge in cognitive neuroscience is understanding how the brain represents and predicts complex, multimodal experiences in naturalistic settings. Traditional neural encoding models, often based on unimodal or static features, fall short in capturing the rich, dynamic structure of real-world cognition.…
Hadi Khalilia, Jahna Otterbacher, Gábor Bella, Shandy Darma + 1 more
Lexical-semantic resources (LSRs), such as online lexicons and wordnets, are fundamental to natural language processing applications as well as to fields such as linguistic anthropology and language preservation. In many languages, however, such resources suffer from quality issues: incorrect entries, incompleteness…
Gabriel Budel, Ying Jin, Piet Van Mieghem, Maksim Kitsak
Interpreting natural language is an increasingly important task in computer algorithms due to the growing availability of unstructured textual data. Natural Language Processing (NLP) applications rely on semantic networks for structured knowledge representation. The fundamental properties of semantic networks must be…
Yevhen Kostiuk, Obdulia Pichardo-Lagunas, Anton Malandii, Grigori Sidorov + 1 more
'Grigori Sidorov' 'Alexander Bolshoy'] In this article, we propose a method for the automatic retrieval of a set of semantic primitive words from an explanatory dictionary and a novel evaluation procedure for the obtained set of primitives. The approach is based on the representation of the dictionary as a directed…
Anna Di Natale, David Garcia
Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this…
Salvatore Citraro, Michael S. Vitevitch, Massimo Stella, Giulio Rossetti
'Giulio Rossetti'] Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms - fragmented across linguistics, psychology and computer…
Mingxue Fu, Guoqiu Chen, Yijie Zhang, Mingzhe Zhang + 1 more
A central challenge in cognitive neuroscience is understanding how the brain represents and predicts complex, multimodal experiences in naturalistic settings. Traditional neural encoding models, often based on unimodal or static features, fall short in capturing the rich, dynamic structure of real-world cognition.…
Emiko J. Muraki, Penny M. Pexman, Richard J. Binney
Multiple representation theories of semantic processing propose that word meaning is supported by simulated sensorimotor experience in modality-specific neural regions, as well as in cognitive systems that involve processing of linguistic, emotional, and introspective information. According to the Hub and Spoke Model…