• Korean Journal of Cognitive Science
• /
• v.19 no.2
• /
• pp.143-161
• /
• 2008

The goal of this paper is to extract the hyponymy relation between words in the corpus. Adopting the basic algorithm of Hearst (1992), I propose a method of pattern-based extraction of semantic relations from the corpus. To this end, I set up a list of hypernym-hyponym pairs from Sejong Electronic Dictionary. This list is supplemented with the superordinate-subordinate terms of CoroNet. Then, I extracted all the sentences from the corpus that include hypemym-hyponym pairs of the list. From these extracted sentences, I collected all the sentences that contain meaningful constructions that occur systematically in the corpus. As a result, we could obtain 21 generalized patterns. Using the PERL program, we collected sentences of each of the 21 patterns. 57% of the sentences are turned out to have hyponymy relation. The proposed method in this paper is simpler and more advanced than that in Cederberg and Widdows (2003), in that using a word net or an electronic dictionary is generally considered to be efficient for information retrieval. The patterns extracted by this method are helpful when we look fer appropriate documents during information retrieval, and they are used to expand the concept networks like ontologies or thesauruses. However, the word order of Korean is relatively free and it is difficult to capture various expressions of a fired pattern. In the future, we should investigate more semantic relations than hyponymy, so that we can extract various patterns from the corpus.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.4
• /
• pp.881-902
• /
• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Proceedings of the Korean Society for Language and Information Conference
• /
• 2002.02a
• /
• pp.320-329
• /
• 2002

The issues in polysemy with respect to the verbs in WordNet will be discussed in this paper. The hypernymy/hyponymy structure of the multiple senses is observed when we try to build a bilingual network for Chinese and English. There are several types of polysemic patterns and a co-hypernym may have the same word form as its subordinates. Fellbaum (2000) dubbed autotroponymy that the verbs linked by mailer relation share the same verb form. However, her syntactic criteria seem not compatible to the hierarchies in WN. Either the criteria or the network should be reconducted. For most verbs in WN 1.7, polysemous relations are unlikely to extend over 3 levels of IS-A relation. Highly polysemous verbs are more complicated and may be involved in certain semantic structures. Semi-automatic sense grouping may be helpful for multimlinguital information retrieveal.

• Journal of KIISE:Software and Applications
• /
• v.28 no.5
• /
• pp.448-448
• /
• 2001

The definitions in Korean dictionary not only describe meanings of title, but also include various semantic information such as hypernymy/hyponymy, meronymy/holonymy, polysemy, homonymy, synonymy, antonymy, and semantic features. This paper purposes to implement a parser as the basic tool to acquire automatically the semantic information from the definitions in Korean dictionary. For this purpose, first we constructed the part-of-speech tagged corpus and the tree tagged corpus from the definitions in Korean dictionary. And then we automatically extracted from the corpora the frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability based on the statistical method. The parser is a kind of the probabilistic chart parser that uses the extracted data. The frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability resolve the noun phrase's structural ambiguity during parsing. The parser uses a grammar factoring, Best-First search, and Viterbi search In order to reduce the number of nodes during parsing and to increase the performance. We experiment with grammar rule's probability, left-to-right parsing, and left-first search. By the experiments, when the parser uses grammar rule's probability and left-first search simultaneously, the result of parsing is most accurate and the recall is 51.74% and the precision is 87.47% on raw corpus.

• Koreanishche Zeitschrift fur Deutsche Sprachwissenschaft
• /
• v.9
• /
• pp.217-236
• /
• 2004

Das Ziel dieser Arbeit ist es, als eine methodische Grundlage zur Erstellung des koreanisch-deutschen WordNets das Grundwissen $\"{u}ber$ das WordNet und einige bisherige Untersuchungen des WordNets darzulegen. Ais erster Schritt wurde einige grundlegende Punkte $f\"{u}r$ das WordNet im Rahmen des WordNets fur Englisch in Betracht gebracht. Dabei ging es um lexikalische Hierarchie, und um semantische Relationen zwischen den Synsets(Zusammensetzen der synonymen $W\"{o}rter$) wie Synonymy, Antonymy, Hyponymy, Mronymy, Troponomy und Entailment. $Anschlie{\ss}end$ wurden EuroNet und GermaNet in kurzer Form vorgestellt, die auf dem Princeton WordNet basierten. EuroNet ist eine multilinguale Datenbasis mit WordNets $f\"{u}r$ einige europaische Sprachen (hollandisch, italienisch, spanisch, deutsch, franzasisch, tschechisch und estnisch). Dieses auf das Deutsch bezogenen WordNet kann wichtige Hinweise $f\"{u}r$ die Erstellung des koreanisch-deutschen WordNets geben. In Korea wurden auch verschiedene Untersuchungen uber das WordNet $f\"{u}r$ Koreanisch unternommen. Darunter kann insbesondere KORTERM WordNet $f\"f{u}r$ Koreanisch als ein umfassendes System $erw\"{a}hnt$ werden, in dem Nomen, Verben, Adjektive und Adverbien miteinander interagieren. KORTERM WordNet fur Koreanisch ist eine multilinguale Datenbasis mit WordNets $f\"{u}r$ einige asiatische Sprachen (koreanisch, japanisch und chinesisch) und versucht noch die weiteren Sprachen in diese multilinguale Datenbasis hineinzubringen. Nach diesem WordNet wird das koreanisch-deutsche WordNet erstellt.