A Chinese Dependency Syntax for Treebanking

PACLIC 20 / Wuhan, China / 1-3 November, 200

An Exploratory Application of Rhetorical Structure Theory to Detect Coherence Errors in L2 English Writing: Possible Implications for Automated Writing Evaluation Software

This paper presents an initial attempt to examine whether Rhetorical Structure Theory (RST) (Mann & Thompson, 1988) can be fruitfully applied to the detection of the coherence errors made by Taiwanese low-intermediate learners of English. This investigation is considered warranted for three reasons. First, other methods for bottom-up coherence analysis have proved ineffective (e.g., Watson Todd et al., 2007). Second, this research provides a preliminary categorization of the coherence errors made by first language (L1) Chinese learners of English. Third, second language discourse errors in general have received little attention in applied linguistic research. The data are 45 written samples from the LTTC English Learner Corpus, a Taiwanese learner corpus of English currently under construction. The rationale of this study is that diagrams which violate some of the rules of RST diagram formation will point to coherence errors. No reliability test has been conducted since this work is at an initial stage. Therefore, this study is exploratory and results are preliminary. Results are discussed in terms of the practicality of using this method to detect coherence errors, their possible consequences about claims for a typical inductive content order in the writing of L1 Chinese learners of English, and their potential implications for Automated Writing Evaluation (AWE) software, since discourse organization is one of the essay characteristics assessed by this software. In particular, the extent to which the kinds of errors detected through the RST analysis match those located by Criterion (Burstein, Chodorow, & Leachock, 2004), a well-known AWE software by Educational Testing Service (ETS), is discussed

Iconizing the Digital Humanities: Models and Modeling from a Semiotic Perspective

Models are ubiquitous in the digital humanities. Against the backdrop of the recent discussion in the philosophy of science about what models are and what they do, this paper presents a semiotic perspective on models in the framework of Charles S. Peirce’s theory of signs that sheds light on the practice of modeling in the digital humanities. As a first step, it is argued that models are icons, i.e. signs that represent their specific objects by being regarded as similar to them; and that there are, in all, three basic types of model, namely “images,” “diagrams,” and “metaphors.” A second step explicates relevant implications of this model-theoretic approach, especially as they relate to the digital humanities. In particular, it is shown that models are not identical to the things they represent and that they only represent them partially; that the representation operates on the basis of a mapping relation between select properties of the model and its object; that each model and each instance of modeling has a theoretical framework; and that models are the true basis for genuine creativity and progress in research

Number agreement, dependency length, and word order in Finnish traditional dialects

In this paper, we research the interaction of number agreement, dependency length, and word order between the subject and the verb in Finnish traditional dialects. While in standard Finnish the verb always agrees with the subject in person and number, in traditional dialects it does not always agree in number with a third person plural subject. We approach this variation with data from The Finnish Dialect Syntax Archive, focusing here on plural lexical subjects. We use generalized linear mixed effects modelling to model variation in number agreement and use as as a predictor the dependency length between the subject and the verb, building in word order as part of this measure. Variation across lemmas, individuals, and dialects is addressed via random grouping factors. Finite verb and the main lexical verb are considered as alternative reference points for dependency length and agreement. The results suggest that the probability of number agreement increases as the distance of the preverbal subject from the verb increases, but the trend is the opposite for postverbal subjects so that the probability of number agreement decreases as the distance of the subject from the verb increases.Peer reviewe

Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics

This thesis is about the problem of compositionality in distributional semantics. Distributional semantics presupposes that the meanings of words are a function of their occurrences in textual contexts. It models words as distributions over these contexts and represents them as vectors in high dimensional spaces. The problem of compositionality for such models concerns itself with how to produce representations for larger units of text by composing the representations of smaller units of text. This thesis focuses on a particular approach to this compositionality problem, namely using the categorical framework developed by Coecke, Sadrzadeh, and Clark, which combines syntactic analysis formalisms with distributional semantic representations of meaning to produce syntactically motivated composition operations. This thesis shows how this approach can be theoretically extended and practically implemented to produce concrete compositional distributional models of natural language semantics. It furthermore demonstrates that such models can perform on par with, or better than, other competing approaches in the field of natural language processing. There are three principal contributions to computational linguistics in this thesis. The first is to extend the DisCoCat framework on the syntactic front and semantic front, incorporating a number of syntactic analysis formalisms and providing learning procedures allowing for the generation of concrete compositional distributional models. The second contribution is to evaluate the models developed from the procedures presented here, showing that they outperform other compositional distributional models present in the literature. The third contribution is to show how using category theory to solve linguistic problems forms a sound basis for research, illustrated by examples of work on this topic, that also suggest directions for future research.Comment: DPhil Thesis, University of Oxford, Submitted and accepted in 201