On the Inherent (non-) Negativity of Negative Sensitive Items. This paper explores the idea that Korean Negative Sensitive Items, which are better viewed as Negative Concord Items (NCIs) (Kim 2001, 2006, Watanabe 2004), should not be construed as inherently negative in spite of the fact that NCIs are able to appear as an elided form without the presence of a negative licenser. Among several diagnostics, which are designed to draw syntactic and semantic distinctions between traditional Negative Polarity Items (NPIs) and NCIs employed in previous studies, the ability of an NCI to appear as a fragment answer raises the question of whether the negativity of NCIs is inherent or not. Contrary to Kim (2001, 2006) and Watanabe (2004), who are in favor of the inherent negativity of NCIs, I claim that non-negative Korean NCIs still need contentful negation to be licensed, and therefore their ability to appear as a fragment answer should be considered as a matter of ellipsis, in support of Giannakidou (2000, 2006). The main argument will be strengthened by the fact that Korean NCIs do not express negative meaning themselves, and that double negation readings are not allowed no matter how many NCIs occur simultaneously.

Distributivity has been one of the central topics in formal semantics. However, no due attention has been paid to embedded distributivity that very frequently occurs in natural languages. In this paper, I propose a formal analysis for embedded distributivity. In analyzing embedded distributivity, I employ no complicated mechanisms but pluralization. Since distributivity is reduced to plurality as Landman (2000) argues, employing plural formation is not an ad hoc approach to embedded distributivity. That is, the plural variable inserted in the process of deriving embedded distributivity is motivated in a principled manner since the pluralization occurs inside a pluralization operator. Moreover, I point out that the plural variable made available is not restricted to entities.

This paper introduces a new linguistic-focused approach for sentiment analysis (SA) of Korean. In order to overcome shortcomings of previous works that focused mainly on statistical methods, we made effective use of various linguistic features reflecting the nature of Korean. These features include contextual shifters, modal affixes, and the morphological dependency of chunk structures. Moreover, in order to eschew possible confusion caused by ambiguous words and to improve the results of SA, we also proposed simple adjustment methods of word senses using KOLON ontology mapping information. Through experiments we contend that effective use of linguistic features and ontological information can improve the results of sentiment analysis of Korean.

This study investigates the semantic and prosodic properties of the so-called contrastive topic. We posit two informational primitives, namely, topical feature [+-T] and focal feature [+-F], from which four different informational categories, i.e., [+T, +F], [+T, -F], [-T, +F], and [-T, -F], are yielded. It is proposed that the informational category of contrastive topic has focal property [+F] as well as topical property [+T]. Based on the semantic approach that regards the function of [+F] as identificational predication and that of [+T] as forming a semantic conditional clause, it is shown that the semantic function of contrastive topic, which is specified as [+T, +F], is the combination of these two functions, i.e., identificational predication in a semantic conditional clause. This is supported by a scrutinized exploration of the prosodic pattern of English contrastive topic.

The aim of this paper is to propose that a category P is encapsulated within a functional layer above the lexical layer, just like vP containing a lexical VP. As is well known, the category P has long been in the obscure domain of syntactic studies: Marantz (2001) and den Dikken (2003), for example, argue that P is a lexical category, but Emonds (1985), Grimshaw (1991), and Baker (2003), maintain that the category P is functional and is a closed category without its own intrinsic meaning. On the other hand, Zwart (2005) argues that it does have some meaning. Following the works of Svenonius (2003, 2006, 2007), and the spirit of Rizzi's (1997) split CP hypothesis, we elaborate and develop Svenonius' idea of split-pP analysis with detailed schematic representations of the novel examples in English, Korean, and Chinese in this paper. Unlike Svenonius, however, this paper incorporates KP into pP-Shell, which is a substantial simplification. Furthermore, Chinese Localizers that have long been considered as Postpositions are now under the category of Prepositions. This proposal renders an X-bar theoretic consistency over the categorical status of Chinese phrasal structures. In short, the present analysis accounts for inconsistency found in English complex preposition phrase (Quirk, et al, 1972, 1985), Chinese circumposition phrase (Ernst 1988, Liu, 2002) and Korean postposition phrase in a unified and consistent manner. Furthermore, by proposing a finer-grained phrasal architecture for the category P, the controversial status of the category subsides within this analysis.

This paper discusses mixed agreement triggered by hybrid pronouns. Hybrid pronouns considered in this paper show number discrepancy in that they are plural in form but singular in meaning. When predicates agree with these hybrid pronouns, the puzzle of number agreement arises: finite verbs show syntactic agreement, while predicate adjectives show semantic agreement. This is explained by three factors in grammar of agreement, the feature specification of agreement controllers, the types of agreement targets, and the Agreement Marking Principle that mediates the relation of two poles of agreement, controllers and targets.

In this paper, I show that anaphora across speakers shows both dynamic and static sides. To capture them all formally, I will adopt semantics based on the assumption that variables range over individual concepts that connect epistemic alternatives. As information increases, a variable can take a different range of possible individual concepts. This is captured by the notion of virtual individual (= vi), a set of individual concepts which are indistinguishable in an information state. The use of a pronoun involves two information states, one for the antecedent, which is always part of the common ground, and the other for the pronoun. Information increase changes vis for variables in the common ground. A pronoun can be used felicitously if there is a unique virtual individual in the information state for the antecedent which does not split in two or more distinctive virtual individuals in the information state for the pronoun. The felicity condition for cross-speaker anaphora can be satisfied in declaratives involving modality, interrogatives and imperatives in a rather less demanding way, because in these cases the utterance does not necessarily require non-trivial personal information for proper use of a pronoun.

This article reports an attempt to perform a similarity analysis on a collection of 25 culinary manuscripts in Chosun periods using a set of quantitative text analysis methods. Historical culinary texts are valuable resources for linguistic, historic, and cultural studies. We consider the similarity of two texts as the distributional similarities of the functional components of the texts. In the case of culinary texts, text elements such as food names, cooking methods, and ingredients are regarded as functional components. We derive the similarity information from the distributional characteristics of the two key functional components, cooking methods and ingredients. The results are also quantified and visualized to achieve a better understanding of the properties of the individual texts and the collection of the texts as a whole.

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.