Looking Beyond the Canonical Formulation and Evaluation Paradigm of Prepositional Phrase Attachment

Prepositional phrase attachment has long been considered one of the most difficult tasks in automated syntactic parsing of natural language text. In this thesis, we examine several aspects of what has become the dominant view of PP attachment in natural language processing with an eye toward extending this view to a more realistic account of the problem. In particular, we take issue with the manner in which most PP attachment work is evaluated, and the degree to which traditional assumptions and simplifications no longer allow for realistically meaningful assessments. We also argue for looking beyond the canonical subset of attachment problems, where almost all attention has been focused, toward a fuller view of the task, both in terms of the types of ambiguities addressed and the contextual information considered

Designing Statistical Language Learners: Experiments on Noun Compounds

The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: (i) it identifies a new class of designs by specifying an architecture for natural language analysis in which probabilities are given to semantic forms rather than to more superficial linguistic elements; and (ii) it explores the development of a mathematical theory to predict the expected accuracy of statistical language learning systems in terms of the volume of data used to train them. The theoretical work is illustrated by applying statistical language learning designs to the analysis of noun compounds. Both syntactic and semantic analysis of noun compounds are attempted using the proposed architecture. Empirical comparisons demonstrate that the proposed syntactic model is significantly better than those previously suggested, approaching the performance of human judges on the same task, and that the proposed semantic model, the first statistical approach to this problem, exhibits significantly better accuracy than the baseline strategy. These results suggest that the new class of designs identified is a promising one. The experiments also serve to highlight the need for a widely applicable theory of data requirements.Comment: PhD thesis (Macquarie University, Sydney; December 1995), LaTeX source, xii+214 page

Diagnosing Reading strategies: Paraphrase Recognition

Paraphrase recognition is a form of natural language processing used in tutoring, question answering, and information retrieval systems. The context of the present work is an automated reading strategy trainer called iSTART (Interactive Strategy Trainer for Active Reading and Thinking). The ability to recognize the use of paraphrase—a complete, partial, or inaccurate paraphrase; with or without extra information—in the student\u27s input is essential if the trainer is to give appropriate feedback. I analyzed the most common patterns of paraphrase and developed a means of representing the semantic structure of sentences. Paraphrases are recognized by transforming sentences into this representation and comparing them. To construct a precise semantic representation, it is important to understand the meaning of prepositions. Adding preposition disambiguation to the original system improved its accuracy by 20%. The preposition sense disambiguation module itself achieves about 80% accuracy for the top 10 most frequently used prepositions. The main contributions of this work to the research community are the preposition classification and generalized preposition disambiguation processes, which are integrated into the paraphrase recognition system and are shown to be quite effective. The recognition model also forms a significant part of this contribution. The present effort includes the modeling of the paraphrase recognition process, featuring the Syntactic-Semantic Graph as a sentence representation, the implementation of a significant portion of this design demonstrating its effectiveness, the modeling of an effective preposition classification based on prepositional usage, the design of the generalized preposition disambiguation module, and the integration of the preposition disambiguation module into the paraphrase recognition system so as to gain significant improvement

Formulaic language

The notion of formulaicity has received increasing attention in disciplines and areas as diverse as linguistics, literary studies, art theory and art history. In recent years, linguistic studies of formulaicity have been flourishing and the very notion of formulaicity has been approached from various methodological and theoretical perspectives and with various purposes in mind. The linguistic approach to formulaicity is still in a state of rapid development and the objective of the current volume is to present the current explorations in the field. Papers collected in the volume make numerous suggestions for further development of the field and they are arranged into three complementary parts. The first part, with three chapters, presents new theoretical and methodological insights as well as their practical application in the development of custom-designed software tools for identification and exploration of formulaic language in texts. Two papers in the second part explore formulaic language in the context of language learning. Finally, the third part, with three chapters, showcases descriptive research on formulaic language conducted primarily from the perspectives of corpus linguistics and translation studies. The volume will be of interest to anyone involved in the study of formulaic language either from a theoretical or a practical perspective

Theories and methods

The notion of formulaicity has received increasing attention in disciplines and areas as diverse as linguistics, literary studies, art theory and art history. In recent years, linguistic studies of formulaicity have been flourishing and the very notion of formulaicity has been approached from various methodological and theoretical perspectives and with various purposes in mind. The linguistic approach to formulaicity is still in a state of rapid development and the objective of the current volume is to present the current explorations in the field. Papers collected in the volume make numerous suggestions for further development of the field and they are arranged into three complementary parts. The first part, with three chapters, presents new theoretical and methodological insights as well as their practical application in the development of custom-designed software tools for identification and exploration of formulaic language in texts. Two papers in the second part explore formulaic language in the context of language learning. Finally, the third part, with three chapters, showcases descriptive research on formulaic language conducted primarily from the perspectives of corpus linguistics and translation studies. The volume will be of interest to anyone involved in the study of formulaic language either from a theoretical or a practical perspective

An Investigation into Automatic Translation of Prepositions in IT Technical Documentation from English to Chinese

Machine Translation (MT) technology has been widely used in the localisation industry to boost the productivity of professional translators. However, due to the high quality of translation expected, the translation performance of an MT system in isolation is less than satisfactory due to various generated errors. This study focuses on translation of prepositions from English into Chinese within technical documents in an industrial localisation context. The aim of the study is to reveal the salient errors in the translation of prepositions and to explore possible methods to remedy these errors. This study proposes three new approaches to improve the translation of prepositions. All approaches attempt to make use of the strengths of the two most popular MT architectures at the moment: Rule-Based MT (RBMT) and Statistical MT (SMT). The approaches include: firstly building an automatic preposition dictionary for the RBMT system; secondly exploring and modifing the process of Statistical Post-Editing (SPE) and thirdly pre-processing the source texts to better suit the RBMT system. Overall evaluation results (both human evaluation and automatic evaluation) show the potential of our new approaches in improving the translation of prepositions. In addition, the current study also reveals a new function of automatic metrics in assisting researchers to obtain more valid or purpose-specific human valuation results

Anaphora resolution for Arabic machine translation :a case study of nafs

PhD ThesisIn the age of the internet, email, and social media there is an increasing need for processing online information, for example, to support education and business. This has led to the rapid development of natural language processing technologies such as computational linguistics, information retrieval, and data mining. As a branch of computational linguistics, anaphora resolution has attracted much interest. This is reflected in the large number of papers on the topic published in journals such as Computational Linguistics. Mitkov (2002) and Ji et al. (2005) have argued that the overall quality of anaphora resolution systems remains low, despite practical advances in the area, and that major challenges include dealing with real-world knowledge and accurate parsing. This thesis investigates the following research question: can an algorithm be found for the resolution of the anaphor nafs in Arabic text which is accurate to at least 90%, scales linearly with text size, and requires a minimum of knowledge resources? A resolution algorithm intended to satisfy these criteria is proposed. Testing on a corpus of contemporary Arabic shows that it does indeed satisfy the criteria.Egyptian Government