Jia-Fei Hong

Ontology-based event relation prediction: A SUMO based study of Mandarin VV compounds

This paper explores the interaction between eventive information and morpho-syntax based on Chinese VV compounds. Chinese VV Compounds’ identical morpho-syntactic structure represents different event relations between the two component words and the correct interpretation of the meaning of these compounds relies on the prediction on their event relations. Without overt syntactic clues, we propose that ontology-based conceptual classification can be used to predict the event relation between the two component words. Compounding is the most productive way to research multi-word expressions in Mandarin Chinese. A Mandarin VV compound can be classified according to the eventive relation between two simplex verbs, which specifies how the eventive meanings of the two simplex verbs combine to form the meaning of the compound. The way in which two events combine with each other depends upon their event types, and the three types of eventive relations that we deal with in this paper are coordinate, modificational, and resultative. Using an ontology-based prediction approach, we hypothesized that the eventive relations could be predicted by the conceptual classification of the two simplex verbs’ event types. First, we utilized SUMO and Sinica BOW to classify each simplex verb. Next, the correlation between the ontology-based classification of each verb position and each eventive type was scored using a manually tagged lexical database and a training set was established. Finally, we encoded the ontological information of each VV compound in a 3-tuple based on these correlation scores. This 3-tuple was represented as a three-dimensional vector and was used to predict the eventive type of the new VV compounds. The results of our findings show that the classification experiments on event relation of unknown VV compounds can be reliably predicted based on the ontological classification of their component words.

Event Selection and Coercion of Two Verbs of Ingestion: A MARVS Perspective

Event semantics in general and event type coercion in particular have been a challenging yet rewarding topic in verbal semantics [1]. However, there have been few corpus-based empirical accounts discussing the range of event type coercions based on the lexical meanings of the verbs. In this paper, we explore the possible types of event coercions for two verbs of ingestion in Mandarin Chinese. In particular, we will show that the different types of coercions can be predicted by the bifurcation of event-internal and role-internal attributes proposed in the MARVS theory [2]. Data examined are taken from the Chinese Gigaword Corpus [3, 4] and accessed through Chinese Word Sketch [5].

Design and prototype of a large-scale and fully sense-tagged corpus

Sense tagged corpus plays a very crucial role to Natural Language Processing, especially on the research of word sense disambiguation and natural language understanding. Having a large-scale Chinese sense tagged corpus seems to be very essential, but in fact, such large-scale corpus is the critical deficiency at the current stage. This paper is aimed to design a large-scale Chinese full text sense tagged Corpus, which contains over 110,000 words. The Academia Sinica Balanced Corpus of Modern Chinese (also named Sinica Corpus) is treated as the tagging object, and there are 56 full texts extracted from this corpus. By using the N-gram statistics and the information of collocation, the preparation work for automatic sense tagging is planned by combining the techniques and methods of machine learning and the probability model. In order to achieve a highly precise result, the result of automatic sense tagging needs the touch of manual revising.

Transliterated Named Entity Recognition Based on Chinese Word Sketch

One unique challenge in Chinese Language Processing is cross-strait named entity recognition. Due to the adoption of different transliteration strategies, foreign name transliterations can vary greatly between the PRC and Taiwan, creating difficulties in NLP tasks including data mining, translation and information retrieval. In this paper, we introduce a novel approach to automatic extraction of divergent transliterations of foreign named entities that bootstraps co-occurrence statistics from tagged Chinese corpora, thereby producing higher precision.

Chinese Conjunctions in Second Language Learners’ Written Texts

In Chinese texts, cohesion refers to grammatical or lexical relationships within sentences and texts. Through these relationships, a series of sentences are connected to form unified texts that are intelligible and meaningful. Various studies have found that cohesion is a crucial factor in readability and reading comprehension, and thus have maintained that cohesion in a text influences comprehension [1]. Among the recent studies on Chinese readability [2, 3, 4, 5], most have overlooked the function of discourse connectives for better reading comprehension. In this study, five Chinese conjunction types taken from a Chinese Written Corpus (CWC) were analyzed to determine their semantic features and structures and the reasons for learners’ usage errors. The results of this study will contribute to the development of teaching Chinese writing by using Chinese conjunctions to improve learners’ writing abilities.

Emotion Lexicon and Its Application: A Study Based on Written Texts

Compared with other language forms, journalese, which is characterized as formal, serious, brief, and standard, in Chinese written texts, carries rich information using few words. Expressing textual meanings, especially personal emotions, by means of appropriate words is quite important in Chinese writing. In teaching Chinese writing, it is easier to compose appropriate texts if teachers and learners interpret emotion words through sense divisions, semantic features, related words, and collocations. This study aimed to build a Chinese emotion lexicon that distinguishes emotion expressions in contexts through the classification of semantic features, as well as provides information via related words and collocations. Moreover, this study applied the characteristics of Chinese emotion words to teach Chinese writing with the aim of improving learners’ writing.

A Study of Chinese Sensation Verbs Used in Linguistic Synaesthesia

Synaesthesia is a well-known phenomenon, both as a neural disorder (The Man Who Tasted Shapes) and a device for linguistic metaphors. The neural basis of synaesthesia is characterized by sensation stimuli or cognition that induces a different cognition spontaneously and involuntarily. Sensation verbs are rich and varied in the Chinese lexicon, but so far there has been no extensive study concerning their use in linguistic synaesthesia. To address this gap in the literature, this study will investigate linguistic synaesthesia using the visual verbs “kan4 (look)” and “jian4 (look)”. Moreover, a discussion on semantic mappings and metaphors will be presented.

Chinese Near-Synonym Study Based on the Chinese Gigaword Corpus and the Chinese Learner Corpus

The study of Chinese near-synonyms is crucial in Chinese lexical semantics, as well as in Chinese language teaching. Recently, Chinese near-synonyms have become important in teaching Chinese as a foreign language; therefore, it is worthwhile to focus on effective strategies for teaching near-synonyms to Chinese learners, especially in recognizing and using lexical senses for nearly synonymous Chinese words. This study will use the Chinese Gigaword Corpus [1] with the Chinese Word Sketch Engine [2] and the Chinese Learner Corpus (of Written Chinese) [3] to compare the usages of nearly synonymous Chinese words by Chinese learners. This study will focus on two sets of near-synonyms—“bian4li4” versus “fang1bian4” and “ren4shi4” versus “zhi1dao4”—as the research objects and discuss their differences in Chinese language teaching.

Event Structure of Transitive Verb: A MARVS Perspective

Module-Attribute Representation of Verbal Semantics (MARVS) is a theory of the representation of verbal semantics that is based on Mandarin Chinese data. In the MARVS theory, there are two different types of modules: Event Structure Modules and Role Modules. There are also two sets of attributes: Event-Internal Attributes and Role-Internal Attributes, which are linked to the Event Structure Module and the Role Module, respectively. In this study, we focus on four transitive verbs as chi1 “eat”, wan2 “play”, huan4 “change” and shao1 “burn” and explore their event structures by the MARVS theory.