• Language and Information
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• v.16 no.2
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• pp.17-42
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• 2012

The issues of compositionality, materialized ever since Frege (1982), are critically re-examined in language first mainly and then in all other possible representational systems such as thoughts, concept combination, computing, gesture, music, and animal cognition. The notion is regarded as necessary and suggested as neurologically correlated in humans, even if a weakened version is applicable because of non-articulated constituents and contextuality. Compositionality is crucially involved in all linguistically or non-linguistically meaningful expressions, dealing with at-issue content, default content, and even not-at-issue meanings such as implicatures and presuppositions in discourse. It is a constantly guiding principle to show the relation between representation and mind, still posing tantalizing research issues.

• Korean Journal of English Language and Linguistics
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• v.3 no.4
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• pp.527-545
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• 2003

I look at the respective responsibilities of different components of the language faculty in the description of two radically different kinds of linguistic phenomenon. The first is the production/perception mismatch in the child's acquisition of the phonology of its first language. There is strong evidence that the child's lexical representations are the same as the adult's, but I argue that the child's own pronunciations, have no linguistic status and are best treated as the product of a neural network. The second is the nature of compositionality, where I argue that compositionality in Natural Language is derivative from that in the Language of Thought. With this assumption and using evidence from quantification in ‘backward control’ structures, I argue that chain theory is intrinsically inimical to a simple view of the legibility relation between LF and LoT.

• Proceedings of the Korean Society for Language and Information Conference
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• 1985.02a
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• pp.95-105
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• 1985

• Annual Conference on Human and Language Technology
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• 1998.10c
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• pp.191-197
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• 1998

은유와 환유에 의해 생성된 문장들은 합성성의 원칙(compositionality principle)에 맞지 않기 때문에 전통적인 언어학에서 많이 다루어지지 않았다. 그러나, Lakoff(Lakoff & Johnson 1980; Lakoff 1987, 1993)와 그의 동료들에 의해서 은유에 대한 인지언어학적인 접근이 시도되면서 활발히 연구되기 시작했다. 그들에 의하면 인간의 일상언어의 많은 표현들이 은유에 의해서 생성되며, 인간의 인지체계가 개념적 은유 (conceptual metaphor)로 이루어져 있다고 주장한다. 본 논문은 화(anger)의 감정을 나타내기 위한 한국어 표현들을 분석하여 인간의 인지체계의 은유적인 양상을 밝혀보고자 한다.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.455-467
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• 2016

This paper presents the constituent-based approach for aligning bilingual multiword expressions, such as noun phrases, by considering the relationship not only between source expressions and their target translation equivalents but also between the expressions and constituents of the target equivalents. We only considered the compositional preferences of multiword expressions and not their idiomatic usages because our multiword identification method focuses on their collocational or compositional preferences. In our experimental results, the constituent-based approach showed much better performances than the general method for extracting bilingual multiword expressions. For our future work, we will examine the scoring method of the constituent-based approach in regards to having the best performance. Moreover, we will extend target entries in the evaluation dictionaries by considering their synonyms.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.32-38
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• 2017

Determining the correct word sense among ambiguous senses is essential for semantic analysis. One of the models for word sense disambiguation is the word space model which is very simple in the structure and effective. However, when the context word vectors in the word space model are merged into sense vectors in a sense inventory, they become typically very large but still suffer from the lexical scarcity. In this paper, we propose a word sense disambiguation method using word embedding that makes the sense inventory vectors compact and efficient due to its additive compositionality. Results of experiments with a Korean sense-tagged corpus show that our method is very effective.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.2-27
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• 2002

As pact of a long-ranged project that aims at establishing database-theoretic semantics as a model of computational semantics, this presentation focuses on the development of a syntactic component for processing strings of words or sentences to construct semantic data structures. For design arid modeling purposes, the present treatment will be restricted to the analysis of some problematic constructions of Korean involving semi-free word order, conjunction arid temporal anchoring, and adnominal modification and antecedent binding. The present work heavily relies on Hausser's (1999, 2000) SLIM theory for language that is based on surface compositionality, time-linearity arid two other conditions on natural language processing. Time-linear syntax for natural language has been shown to be conceptually simple and computationally efficient. The associated semantics is complex, however, because it must deal with situated language involving interactive multi-agents. Nevertheless, by processing input word strings in a time-linear mode, the syntax cart incrementally construct the necessary semantic structures for relevant queries and valid inferences. The fragment of Korean syntax will be implemented in Malaga, a C-type implementation language that was enriched for both programming and debugging purposes arid that was particluarly made suitable for implementing in Left-Associative Grammar. This presentation will show how the system of syntactic rules with constraining subrules processes Korean sentences in a step-by-step time-linear manner to incrementally construct semantic data structures that mainly specify relations with their argument, temporal, and binding structures.

• ETRI Journal
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• v.10 no.4
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• pp.99-117
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• 1988

This paper has two main purposes, One is a scientific theory for the Hangul writing system. It is a mathematical model which is formulated a logical characteristics involved in the compositionality principle of Hun Min Jung Um[6], and is based on the traditional scientific methodology and theory. The other is an engineering model for the Hangul writing system. It has proven that the model is more theoretical and practical that the conventional models called Hangul automata. This paper also shows the reasons why Korean has a great national pride in Hangul[3]. Consequently, we suggest that Hangul writing system has a potential capability as an ultimate human interface connecting human, computer and information.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.