19 papers · ranked by Valyu relevance
Colleen E. Crangle, Marcos Perreau-Guimaraes, Patrick Suppes, Kevin Paterson
'Kevin Paterson'] This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one…
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…
Pascual Julián-Iranzo, Fernando Sáenz-Pérez
This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be…
Vuong M. Ngo, Tru H. Cao, Tuan M. V. Le
Text search based on lexical matching of keywords is not satisfactory due to polysemous and synonymous words. Semantic search that exploits word meanings, in general, improves search performance. In this paper, we survey WordNet-based information retrieval systems, which employ a word sense disambiguation method to…
Devendra Singh Chaplot, Ruslan Salakhutdinov
Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the…
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)…
Maksymilian Bujok, Piotr Fronczak, Agata Fronczak
Wordnets are semantic networks containing nouns, verbs, adjectives, and adverbs organized according to linguistic principles, by means of semantic relations. In this work, we adopt a complex network perspective to perform a comparative analysis of the English and Polish wordnets. We determine their similarities and…
Ivy Zhou, Tijl Grootswagers, Blake Segula, Amanda Robinson + 3 more
Guided by the observation that similar words in language occur in similar contexts, linguistic computational models trained on statistics of word co-occurrence in texts were shown to be effective in modelling both human performance in psycholinguistic tasks and semantically imbued representations in the brain. But, it…
Roni Mittelman, Min Sun, Benjamin Kuipers, Silvio Savarese
Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical structure of categories and concepts, has been shown to provide a key prior which allows for optimal predictions. The…
Khang Nhứt Lâm, Feras Al Tarouti, Jugal Kalita
Manually constructing a Wordnet is a difficult task, needing years of experts' time. As a first step to automatically construct full Wordnets, we propose approaches to generate Wordnet synsets for languages both resource-rich and resource-poor, using publicly available Wordnets, a machine translator and/or a single…
Peter D. Turney, Saif M. Mohammad, Richard A Blythe
We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future…
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…
Mario Jarmasz, Stan Śzpakowicz
This paper presents the steps involved in creating an electronic lexical knowledge base from the 1987 Penguin edition of Roget's Thesaurus. Semantic relations are labelled with the help of WordNet. The two resources are compared in a qualitative and quantitative manner. Differences in the organization of the lexical…
Songsheng Ying, Sabine Ploux
The word embeddings related to paradigmatic and syntagmatic axes are applied in an fMRI encoding experiment to explore human brain’s activity pattern during story listening. This study proposes the construction of paradigmatic and syntagmatic semantic embeddings respectively by transforming WordNet-alike knowledge…
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…
Taicheng Huang, Zonglei Zhen, Jia Liu
Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts and construct nested hierarchical structures. Similarly, deep convolutional neural networks (DCNNs) can learn to recognize objects as perfectly as human; yet it is unclear whether they can learn semantic…
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…
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…
Jona Sassenhagen, Christian J. Fiebach
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and…