• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.103-112
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• 2002

This paper aims to give an explanation of the combination of certain nouns and the verb ha-'do'. Although the verb ha-'do'normally takes an event type argument, it takes some substantival nouns such as paiolin 'violin', umsikcem 'restaurant', and so on. A substantival noun undergoes type shifting, because the governing verb ha-'do'coerces an entity type noun to an event reading, taking missing information from the qualia of the entity type noun. In addition, some nouns like ppallay 'laundry'are dot objects. The verb laking a dot objects selects a proper type between multiple subtypes of the dot object. Type pumping operation makes that selection possible.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.223-233
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• 2007

The so-called Korean BNC (bound noun construction) displays complex syntactic, semantic, and constructional properties. This paper, couched upon a constraint-based approach, two different syntactic structures for the construction with articulated lexical properties for the BNs and relevant predicates. The paper reports an implementation of this analysis in the LKB (Linguistic Knowledge Building) system and shows us that this direction is robust enough to pare relevant sentences.

• Language and Information
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• v.11 no.1
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• pp.39-52
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• 2007

Noun phrase coordination is an extremely productive phenomenon. Based on an observation that conjuncts tend to denote semantically related concepts, we collect four hundred thousand pairs of conjuncts from the British National Corpus, in an attempt to build an is-a hierarchy of English noun concepts. The modifiedness patterns of the two words in these pairs point to three distinct semantic relations: sibling, cousin, and ancestor-or-ancestor' sibling. The process of finding them and how these pairs are used to motivate groups of quasi-synonyms and then to locate the hypernyms are discussed.

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.75-78
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• 2003

The paper deals with experimental results which was performed to investigate the characteristics of Korean lexical processing and representation of morphemes involved in Korean noun and verb Eojeols. The investigation is also related with the 'English past tense debate' which deals with human mental computation. Experiments using fMRI methods, show that Korean noun Eojeols and both regular and irregular verb Eojeols show a similar activation pattern. Thus, the results indicate that the morphological processing in Korean noun and verb Eojeols are performed quite differently than the Indo-European morphological processing.

• Language and Information
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• v.12 no.1
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• pp.85-103
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• 2008

Like English, Korean employs several complicated types of cleft constructions. This paper deals with two main types of Korean cleft constructions: predicational and identificational. It first reviews the formal properties of these two types and then provides a constraint-based analysis that can be computationally implemented. In particular, the paper assumes two types of noun KES (one as a common noun and the other as a bound noun) and treats the argument-gapped cleft clause similar to relative clauses while treating the adjunct-gapped cleft clause as a noun-complement construction. The paper further shows that the cleft constructions are closely linked to the copula constructions, sharing many common properties while having their own constructional constraints.

• Korean Linguistics
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• v.80
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• pp.151-175
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• 2018

This paper proposed a method of semantic recognition to improve the extraction of correct answers of the Q&A system through machine learning. For this purpose, the semantic recognition method is described based on the distribution of predicative nouns. Predicative noun vocabularies and sentences were collected from Wikipedia documents. The predicative nouns are typed by analyzing the environment in which the predicative nouns appear in sentences. This paper proposes a semantic recognition method of predicative nouns to which rules can be applied. In Chapter 2, previous studies on predicative nouns were reviewed. Chapter 3 explains how predicative nouns are distributed. In this paper, every predicative nouns that can not be processed by rules are excluded, therefore, the predicative nouns noun forms combined with the case marker '의' were excluded. In Chapter 4, we extracted 728 sentences composed of 10,575 words from Wikipedia. A semantic analysis engine tool of ETRI was used and presented a predicative nouns noun that can be handled semantic recognition language.

• Cross-Cultural Studies
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• v.36
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• pp.271-291
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• 2014

In this paper, we maintain that case makers '에e' and '에게ege', and '에서eseo' and '에게서egeseo' are not two separate morphemes but are simply allomorphs of the same morphemes respectively. When '에e' and '에게ege' are used as a dative marker, they show exactly the same semantic function and are in complementary distribution in relation to the semantic features of their preceding noun; that is, if the preceding noun is an animate noun, '에게ege' is used and '에e' is used if not. Also, '에게서egeseo' and '에서eseo' as ablative and locative case makers show exactly the same semantic function and show complementary distribution depending on whether the preceding noun is animate or non-animate. Therefore, we assume that these markers are semantically conditioned allomorphs.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.93-102
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• 2022

Dependency parsing is a decision problem of the syntactic relation between words in a sentence. Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space. However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space. To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit. Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM. Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model. The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.769-783
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• 2000

In this paper, in order to disambiguate Korean noun word sense, we define a local context and explain how to extract it from a raw corpus. Following the intuition that two different nouns are likely to have similar meanings if they occur in the same local context, we use, as a clue, the word that occurs in the same local context where the target noun occurs. This method increases the usability of extracted knowledge and makes it possible to disambiguate the sense of infrequent words. And we can overcome the data sparseness problem by extending the verbs in a local context. The sense of a target noun is decided by the maximum similarity to the clues learned previously. The similarity between two words is computed by their concept distance in the sense hierarchy borrowed from WordNet. By reducing the multiplicity of clues gradually in the process of computing maximum similarity, we can speed up for next time calculation. When a target noun has more than two local contexts, we assign a weight according to the type of each local context to implement the differences according to the strength of semantic restriction of local contexts. As another knowledge source, we get a co-occurrence information from dictionary definitions and example sentences about the target noun. This is used to support local contexts and helps to select the most appropriate sense of the target noun. Through experiments using the proposed method, we discovered that the applicability of local contexts is very high and the co-occurrence information can supplement the local context for the precision. In spite of the high multiplicity of the target nouns used in our experiments, we can achieve higher performance (89.8%) than the supervised methods which use a sense-tagged corpus.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.115-125
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• 2016

Novels includes various character names, depending on the genre and the spatio-temporal background of the novels and the nationality of characters. Besides, characters and their names in a novel are created by the author's pen and imagination. As a result, any proper noun dictionary cannot include all kind of character names which have been created or will be created by authors. In addition, since Korean does not have capitalization feature, character names in Korean are harder to detect than those in English. Fortunately, however, Korean has postpositions, such as "-ege" and "hante", used by a sentient being or an animate object (noun). We call such postpositions as animate postpositions in this paper. In a previous study, the authors manually selected character names by referencing both Wikipedia and well-known people dictionaries after utilizing Korean morpheme analyzer, a proper noun dictionary, postpositions (e.g., "-ga", "-eun", "-neun", "-eui", and "-ege"), and titles (e.g., "buin"), in order to extract social networks from three novels translated into or written in Korean. But, the precision, recall, and F-measure rates of character identification are not presented in the study. In this paper, we evaluate the quantitative contribution of animate postpositions to character identification from novels, in terms of precision, recall, and F-measure. The results show that utilizing animate postpositions is a valuable and powerful tool in character identification without a proper noun dictionary from novels translated into or written in Korean.