• Title/Summary/Keyword: Lexical effect

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Gender difference in the sound change of lexical pitch accents of South Kyungsang Korean

  • Lee, Hyunjung
    • Phonetics and Speech Sciences
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    • v.7 no.4
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    • pp.123-130
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    • 2015
  • Given a recent finding showing that female speakers of South Kyungsang Korean is undergoing a sound change of the lexical pitch accent, this study tested whether the change is also reflected for male speech. This study compared F0 scaling and timing properties of accent words produced by younger female and male speakers of South Kyungsang Korean. The results indicated clear gender-related differences, showing more distinct acoustic properties across the accent words for male production compared to females. Despite the better distinction, however, younger male speakers showed peak delay where the F0 peaks are located further to the right compared to conservative speakers' production. Therefore, it might be suggested that younger male speakers' accent productions are in between conservative and innovative phonetic forms.

The Effect of Semantic Neighborhood Density in Korean Visual Word Recognition (한국어 시각단어재인에서 의미 이웃크기 효과)

  • Kwon, You-An;Nam, Ki-Chun
    • Proceedings of the KSPS conference
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    • 2007.05a
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    • pp.173-175
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    • 2007
  • The lexical decision task (LDT) commonly postulates the activation of semantic level. However, there are few studies for the feedback effect from semantic level. The purpose of the present study is to investigate whether the feedback effect from semantic level is facilitatory or inhibitory in Korean LDT. In Experiment 1, we manipulated the number of phonological syllable neighbors (PSN) and the number of semantic neighbors (SEN) orthogonally while orthographic syllable neighbor (OSN) is dense. In the results, the significant facilitatory effect was shown in words with many SEN. In Experiment 2, we examined same conditions as Experiment 1 but OSN was sparse. Although the similar lexical decision latency pattern was shown, there was no statistical significance. These results can be explained by the feedback activation from semantic level. If a target has many SENs and many PSNs, it receives more feedback activation from semantic level than a target with few SENs and PSNs.

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Realizations of Discourse Focus and Structure of Intonation in Japanese (일본어의 초점 실현과 인토네이션의 구조)

  • Choi, Young-Sook
    • Speech Sciences
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    • v.9 no.4
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    • pp.187-200
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    • 2002
  • The purpose of the present study is to see in terms of $F_{0}$ variation in Japanese how discourse focus and the lexical word accent interact with each other in realizing overall intonation patterns. Discourse focus causes prosodic restructuring of phrase structures and, as a result, largely affects pitch contours, whereas the lexical word accent is said to delimit the $F_{0}$ into a certain range. Measurement of $F_{0}$ was made of utterances of Japanese sentences to observe behavior of pitch contours with varied focus assignment and lexical accent specifications. The utterances were obtained in question-answer discourse contexts so that in a sentence, either one NP was always focused or no focus was assigned. I set four points for $F_{0}$ measurement; $F_{1s},F_{1m}, F_{2s}$, and $F_{2m}$, two for each noun phrase corresponding to $F_{0}$ at the beginning of the first syllable and that of the vocalic portion of the second syllable in the two NP's. The results of present study were as follows: (1) for all combination of lexical accent types, the $F_{0}$ rise both in NP1 and NP2 are higher when focused than when not focused. (2) NP2 starts a new accentual phrase when focused, showing even higher $F_{0}$ than NP1, the latter of which implies that in forming a new accentual phrase by focusing, catathesis does not seem to take effect on NP2 preceded by accented NP1. (3) unfocused NP2 preceded by unaccented NP1 has higher $F_{0}$ than those preceded by accented NP1.

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A Comparison Study between Human and Computation Model on Language Phenomena of in Korean Lexical Decision Task (한국어 어휘판단과제와 관련된 언어현상의 인간과 계산주의 모델의 비교)

  • Lim, Heui-Seok;Kwon, You-An;Park, Ki-Nam
    • Proceedings of the Korean Society for Cognitive Science Conference
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    • 2006.06a
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    • pp.33-37
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    • 2006
  • 본 논문은 어휘판단과제(LDT: Lexical Decision Task)시 나타나는 여러 언어현상 중 단어빈도효과(word frequency effect)와 단어유사성효과(word similarity effect)를 한국어에 적용시켜 인간과 계산주의적 모델을 통해 실험하고, 결과를 비교하였다. 실험결과 인간과 계산주의적 모델 각각 한국어에 대해 단어빈도효과와 단어 유사성효과를 보였으며, 인간의 실험결과와 계산주의적 모델의 결과가 유의미한 유사성을 나타내었다.

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Semantic Interference Effect;Contrasting the Lexical Competition with the Concept Competition Hypothesis (의미간섭효과;어휘경쟁가설 대 개념경쟁가설의 비교)

  • Koo, Min-Mo;Nam, Ki-Chun
    • Proceedings of the KSPS conference
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    • 2007.05a
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    • pp.74-77
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    • 2007
  • In order to compare two hypotheses on the origin of semantic interference effect that has been offered in the psycholinguistic literature, we conducted two experiments using the picture-word interference paradigm. When participants named the pictures of the objects simultaneously presented with distractor words, they were required to use either native words (Experiment 1) or loanwords (Experiment 2). The pictures were paired with three kinds of distractor words that were identical, semantically related and neutral to the picture. Two observations were obtained from two experiments. Firstly, the naming times of the pictures were more fast in context of the identical distractors than in context of the neutral ones. Secondly, naming times were more slow in the presence of the semantically related distractors relative to the neutral ones. These findings support the claim that semantic interference is based on a lexical retrieval conflict.

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A Comparison Study between Human and Computation Model on Language Phenomena of in Korean Lexical Decision Task (한국어 어휘판단과제와 관련된 언어현상의 인간과 계산주의 모델의 비교)

  • Park, Ki-Nam;Lim, Heui-Seok
    • Proceedings of the KAIS Fall Conference
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    • 2006.05a
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    • pp.391-393
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    • 2006
  • 본 논문은 어휘판단과제(LDT: Lexical Decision Task)시 나타나는 여러 언어현상 중 단어빈도효과(word frequency effect)와 단어유사성효과(word similarity effect)를 한국어에 적용시켜 인간과 계산 주의적 모델을 통해 실험하고, 결과를 비교하였다. 실험결과 인간과 계산주의적 모델 각각 한국어에 대해 단어빈도효과와 단어 유사성효과를 보였으며, 인간의 실험결과와 계산주의적 모델의 결과가 유의미한 유사성을 나타내었다.

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The Effect of Expert Reviews on Consumer Product Evaluations: A Text Mining Approach (전문가 제품 후기가 소비자 제품 평가에 미치는 영향: 텍스트마이닝 분석을 중심으로)

  • Kang, Taeyoung;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.22 no.1
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    • pp.63-82
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    • 2016
  • Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

Restricting Answer Candidates Based on Taxonomic Relatedness of Integrated Lexical Knowledge Base in Question Answering

  • Heo, Jeong;Lee, Hyung-Jik;Wang, Ji-Hyun;Bae, Yong-Jin;Kim, Hyun-Ki;Ock, Cheol-Young
    • ETRI Journal
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    • v.39 no.2
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    • pp.191-201
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    • 2017
  • This paper proposes an approach using taxonomic relatedness for answer-type recognition and type coercion in a question-answering system. We introduce a question analysis method for a lexical answer type (LAT) and semantic answer type (SAT) and describe the construction of a taxonomy linking them. We also analyze the effectiveness of type coercion based on the taxonomic relatedness of both ATs. Compared with the rule-based approach of IBM's Watson, our LAT detector, which combines rule-based and machine-learning approaches, achieves an 11.04% recall improvement without a sharp decline in precision. Our SAT classifier with a relatedness-based validation method achieves a precision of 73.55%. For type coercion using the taxonomic relatedness between both ATs and answer candidates, we construct an answer-type taxonomy that has a semantic relationship between the two ATs. In this paper, we introduce how to link heterogeneous lexical knowledge bases. We propose three strategies for type coercion based on the relatedness between the two ATs and answer candidates in this taxonomy. Finally, we demonstrate that this combination of individual type coercion creates a synergistic effect.

The Effect of Strong Syllables on Lexical Segmentation in English Continuous Speech by Korean Speakers (강음절이 한국어 화자의 영어 연속 음성의 어휘 분절에 미치는 영향)

  • Kim, Sunmi;Nam, Kichun
    • Phonetics and Speech Sciences
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    • v.5 no.2
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    • pp.43-51
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    • 2013
  • English native listeners have a tendency to treat strong syllables in a speech stream as the potential initial syllables of new words, since the majority of lexical words in English have a word-initial stress. The current study investigates whether Korean (L1) - English (L2) late bilinguals perceive strong syllables in English continuous speech as word onsets, as English native listeners do. In Experiment 1, word-spotting was slower when the word-initial syllable was strong, indicating that Korean listeners do not perceive strong syllables as word onsets. Experiment 2 was conducted in order to avoid any possibilities that the results of Experiment 1 may be due to the strong-initial targets themselves used in Experiment 1 being slower to recognize than the weak-initial targets. We employed the gating paradigm in Experiment 2, and measured the Isolation Point (IP, the point at which participants correctly identify a word without subsequently changing their minds) and the Recognition Point (RP, the point at which participants correctly identify the target with 85% or greater confidence) for the targets excised from the non-words in the two conditions of Experiment 1. Both the mean IPs and the mean RPs were significantly earlier for the strong-initial targets, which means that the results of Experiment 1 reflect the difficulty of segmentation when the initial syllable of words was strong. These results are consistent with Kim & Nam (2011), indicating that strong syllables are not perceived as word onsets for Korean listeners and interfere with lexical segmentation in English running speech.

Improving the Performance of Korean Text Chunking by Machine learning Approaches based on Feature Set Selection (자질집합선택 기반의 기계학습을 통한 한국어 기본구 인식의 성능향상)

  • Hwang, Young-Sook;Chung, Hoo-jung;Park, So-Young;Kwak, Young-Jae;Rim, Hae-Chang
    • Journal of KIISE:Software and Applications
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    • v.29 no.9
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    • pp.654-668
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    • 2002
  • In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.