Concept hierarchy knowledge, such as hyponymy and meronymy, is very important for various Natural Language Processing systems. While WordNet and Wikipedia are being manually constructed and maintained as lexical ontologies, many researchers have tackled how to extract concept hierarchies from very large corpora of text documents such as the Web not manually but automatically. However, their met...

The Middle English period is well known as one of widespread lexical borrowing from French and Latin, scholarly accounts traditionally assume that this influx loanwords caused many native terms to shift in sense or drop out use entirely. study analyses an extensive dataset, tracking patterns retention, replacement semantic change, comparing long-term outcomes for both non-native words. Our resu...

Patent text is a mixture of legal terms and domain specific terms. Patent writers tend to paraphrase standard terminology with hypernym, hyponym and synonym substitutions in order to avoid narrowing the scope of the patent invention. The practice of paraphrasing affects the exact match retrieval function negatively. There have been many success stories addressing vocabulary mismatching using ps...

During two days at a conference focused on circulatory and respiratory health, 68 volunteers untrained in knowledge engineering participated in an experimental knowledge capture exercise. These volunteers created a shared vocabulary of 661 terms, linking these terms to each other and to a pre-existing upper ontology by adding 245 hyponym relationships and 340 synonym relationships. While ontolo...

Automatic knowledge extraction from such a very large document corpus as the Web is one of the hottest research topics in the domain of Artificial Intelligence and Database technologies. This chapter introduces my object-oriented and the existing methods to extract semantic (e.g., hyponymy and meronymy) and sensory (e.g., visual and aural) knowledge from the Web, and compares them by showing se...

Ontologies conceptualize knowledge with concepts and the relevant relations among them. Domain-independent ontology represents common knowledge of natural language. Domain-specific ontology represents professional knowledge in a specific domain. Information retrieval system purposes to find the useful documents wanted by users. The major problem of information retrieval is ambiguity of language...

Taxonomic relation identification aims to recognize the ‘is-a’ relation between two terms. Previous works on identifying taxonomic relations are mostly based on statistical and linguistic approaches, but the accuracy of these approaches is far from satisfactory. In this paper, we propose a novel supervised learning approach for identifying taxonomic relations using term embeddings. For this pur...

Hypernymy relations (those where an hyponym term shares a “isa” relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, ...

This article focuses on dictionary definitions of meanings personal-masculine nouns and personal-feminine motivated by masculine nouns. The contained in the Wielki slownik jezyka polskiego PAN were analyzed, which, depending need, are compared with relevant Uniwersalny polskiego, observations made this regard constitute title glosses to lexicographic practice. that do not (primarily) indicate g...

FinnWordNet is a Finnish wordnet which complies with the structure of the Princeton WordNet (Fellbaum, 1998). It was created by translating all the words in Princeton WordNet. It is open source and contains over 117 000 synsets. We are now testing different methods in order to improve and expand the content of FinnWordNet. Since wordnets are structured ontologies, a location for a word in FinnW...