• Phonetics and Speech Sciences
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• v.4 no.1
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• pp.55-65
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• 2012

In this study, the prosodic disambiguation of the syntactic attachment differences was investigated in relation to the effect of lexical bias. Speech materials were composed of N1-conj-N2-PP phrases such as "walkers and runners with dogs." The results show that the use of durational pattern is dominant over the pitch pattern to differentiate the attachment differences. The characteristic pitch contour was the rise and fall over N1 and N2 in the high attachment. The pitch contour in the low attachment was the rise and fall over N2 and N3 although the frequency of such patterns was lower for the low attachment case. For the durational pattern, the lengthening in the N2 region plays a significant role in the disambiguation of the syntactic attachments. The interaction between the lexical bias and the syntactic attachment was not statistically significant in the duration data.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Korean Journal of Cognitive Science
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• v.2 no.2
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• pp.279-307
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• 1990

The present research inverstigates the nature of contextual effects on pronoun reference resolution during text comprehesion.Through three experiments,this research examines how various contextuall informations influence on reference resolution and interact with syntactic variables.In experiment 1,the local context was controlled by biasing the pronoun-sentence context towards a certain preceding referent.The lexical decision time and the forced choice time for the correct referent were measured.The results showed that the local contexts have clear effect on reference resolution.The effects of syntactic ambiguity were also observed though the local context was biased towards a certain referent noun.In experiment 2,the global context effect was examined by introducing the text-thematic context in a preceding sentence while keeping the following pronoun-sentence context neutral.The results showed that the global thematic context bias towards a subject or object in a preceding sentence entails a faster response time than the thematically neutral context.In experiment 3,another aspects of context effects were inverstigated by manipulating the consistency of the preceding thematic context with the following pronoun-sentence context.The results showed that the lexical decision responses and forced referent choice responses were faster when the prethematic context and the post-anaphoric context match than when they mismatch.In sum,the overall results of three experiments of this research indicates that context has a clear effect on pronoun reference resolution during text comprehension.

• Lingua Humanitatis
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• v.2 no.2
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• pp.51-84
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• 2002

The 1990s have seen the publication of many books devoted to Language and Ideology (cf. Joseph & Taylor 1990. for one of the early ones) even though the term 'ideology' itself has remained ill-defined (Woolard 1998). The focus of attention has usually been placed on the particular use of language and often for some kind of 'political' ends, not on linguistic or other scholarship which might have been driven by some sort of ideology, i.e., a bundle of assumptions which themselves were taken as given. At least since Edward Said's 1978 book Orientalism, it has been clear to everyone that scholars construct their conceptualization of things in line with their understanding of the cultural, social, and political world in which they live, and that this often unreflected 'pre-understanding' effects their view of cultures that are different from theirs and more often than not geographically and temporally distant from theirs. This recognition has had a sobering effect no doubt, and Said's book has long since become 'mainstream.' Much more disturbing to the scholarly profession has been the publication of Martin Bernal's Black Athena in 1987, since it went much further, going beyond accusations of colonialism and cultural bias, in suggesting that the Western representation of Classical Greece over the past two hundred years was false and that what had been accepted until now about occidental antiquity must now be seen derived from African-Asiatic cultures of the Near East, notably that of the Ancient Egyptians, and that no other than Socrates should be seen as black man. While we may understand the intellectual climate in the United States that led academics to present 'myth as history' (Lefkowitz 1996), it is obvious that lines of regular scholarly principles of investigation have been crossed (cf Lefkowitz & Rogers 1996). The present paper investigates what may be seen as the ideological underpinnings of such work. After reviewing some recent scholarship in the area of linguistic historiography that have shown that academic work has never been 'value-neutral' (as may have been assumed or has been claimed by some practitioners), it is argued that in effect one must be aware of what Clemens Knobloch has recently termed Resonanzbedarf, i.e., the desire, whether conscious or not, of scholars-and probably scientists, too-to have their work recognized by the educated public and that, in so doing, their discourses tend to pick up on contemporary popular notions. These efforts may be harmless if everyone was to recognize these allusions and adoption of certain lexical. items(buzz words) as props or what Germans call Versatzstiicke, but history tells us that this has not always been the case. Still, as Hutton (1999) has shown, not all scholarship during the Third Reich for example can simply be dismissed as worthless because it was conducted in under a prevailing political ideology. Indeed, in seemingly innocent times, linguists can be shown to frame their argument in a way that makes them appear so utterly superior to their predecessors (cf. Lawson 2001). Upon closer inspection, those discourses turn out to be much like those of scholars in nationalistic environments that have tended to select their 'facts' to prove a particular hypothesis (cf., e.g., Koerner 2001). The article argues for scholars to take a more active role in exploding myths, scientifically unfounded claims, and ideologically driven distortions, especially those that are socially and politically harmful.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.