A Formal Model of Ambiguity and its Applications in Machine Translation

Systems that process natural language must cope with and resolve ambiguity. In this dissertation, a model of language processing is advocated in which multiple inputs and multiple analyses of inputs are considered concurrently and a single analysis is only a last resort. Compared to conventional models, this approach can be understood as replacing single-element inputs and outputs with weighted sets of inputs and outputs. Although processing components must deal with sets (rather than individual elements), constraints are imposed on the elements of these sets, and the representations from existing models may be reused. However, to deal efficiently with large (or infinite) sets, compact representations of sets that share structure between elements, such as weighted finite-state transducers and synchronous context-free grammars, are necessary. These representations and algorithms for manipulating them are discussed in depth in depth. To establish the effectiveness and tractability of the proposed processing model, it is applied to several problems in machine translation. Starting with spoken language translation, it is shown that translating a set of transcription hypotheses yields better translations compared to a baseline in which a single (1-best) transcription hypothesis is selected and then translated, independent of the translation model formalism used. More subtle forms of ambiguity that arise even in text-only translation (such as decisions conventionally made during system development about how to preprocess text) are then discussed, and it is shown that the ambiguity-preserving paradigm can be employed in these cases as well, again leading to improved translation quality. A model for supervised learning that learns from training data where sets (rather than single elements) of correct labels are provided for each training instance and use it to learn a model of compound word segmentation is also introduced, which is used as a preprocessing step in machine translation

Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

Binding Phenomena Within A Reductionist Theory of Grammatical Dependencies

This thesis investigates the implications of binding phenomena for the development of a reductionist theory of grammatical dependencies. The starting point is the analysis of binding and control in Hornstein (2001, 2009). A number of revisions are made to this framework in order to develop a simpler and empirically more successful account of binding phenomena. The major development is the rejection of economy-based accounts of Condition B effects. It is argued that Condition B effects derive directly from an anti-locality constraint on A-movement. Competition between different dependency types is crucial to the analysis, but is formulated in terms of a heavily revised version of Reinhart's (2006) "No Sneaking" principle, rather than in terms of a simple economy preference for local over non-local dependencies. In contrast to Reinhart's No Sneaking, the condition presented here ("Keeping Up Appearances") has a phonologically rather than semantically specified comparison set. A key claim of the thesis is that the morphology of pronouns and reflexives is of little direct grammatical import. It is argued that much of the complexity of the contemporary binding literature derives from the attempt to capture the distribution of pronouns and reflexives in largely, or purely, syntactic and semantic terms. The analysis presented in this dissertation assigns a larger role to language-specific "spellout" rules, and to general pragmatic/interpretative principles governing the choice between competing morphemes. Thus, a core assumption of binding theory from LGB onwards is rejected: there is no syntactic theory which accounts for the distribution of pronouns and reflexives. Rather, there is a core theory of grammatical dependencies which must be conjoined with with phonological, morphological and pragmatic principles to yield the distributional facts in any given language. In this respect, the approach of the thesis is strictly non-lexicalist: there are no special lexical items which trigger certain kinds of grammatical dependency. All non-strictly-local grammatical dependencies are formed via A- or A-chains, and copies in these chains are pronounced according to a mix of universal principles and language-specific rules. The broader goal of the thesis is to further the prospects for a "reductionist" approach to grammatical dependencies along these lines. The most detailed empirical component of the thesis is an investigation of the problem posed by binding out of prepositional phrases. Even in a framework incorporating sideward movement, the apparent lack of c-command in this configuration poses a problem. Chapter 3 attempts to revive a variant of the traditional "reanalysis" account of binding out of PP. This segues into an investigation of certain properties of pseudopassivization and preposition stranding. The analyses in this thesis are stated within an informal syntactic framework. However, in order to investigate the precise implications of a particular economy condition, Merge over Move, a partial formalization of this framework is developed in chapter 4. This permits the economy condition to be stated precisely, and in a manner which does not have adverse implications for computational complexity

CLAIRE makes machine translation BLEU no more

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-139).We introduce CLAIRE, a mathematically principled model for inferring ranks and scores for arbitrary items based on forced-choice binary comparisons, and show how to apply this technique to statistical models to take advantage of problem-specific assistance from non-experts. We apply this technique to two language processing problems: parsing and machine translation. This leads to an analysis which casts doubts on modern evaluation methods for machine translation systems, and an application of CLAIRE as a new technique for evaluating machine translation systems which is inexpensive, has theoretical guarantees, and correlates strongly in practice with more expensive human judgments of system quality. Our analysis reverses several major tenants of the mainstream machine translation research agenda, suggesting in particular that the use of linguistic models should be reexamined.by Ali Mohammad.Sc.D

Mathematical linguistics

but in fact this is still an early draft, version 0.56, August 1 2001. Please d

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

A hybrid NLP & semantic knowledgebase approach for the intelligent exploration of Arabic documents

In the contemporary era, a colossal amount of information is published daily on the Web in the form of articles, documents, reviews, blogs and social media posts. As most of this data is available in the form of unstructured documents, it makes it challenging and timeconsuming to extract non-trivial, previously unknown, and potentially useful knowledge from the published documents. Hence, extracting useful knowledge from unstructured text, i.e., Information Extraction, is becoming an increasingly significant aspect of knowledge discovery. This work focuses on Information Extraction form Arabic unstructured text, which is an especially challenging task as Arabic is a highly inflectional and derivational language. The problem is compounded by the lack of mature tools and advanced research in Arabic Natural Language Processing (NLP) in comparison to European languages for instance. The principal objective of this research work is presenting a comprehensive methodology for integrating domain knowledge with Natural Language Processing techniques that were proven effective in solving most classification problems in order to improve the Information extraction process form online unstructured data. The importance of NLP tools lies in that they play a key role in allowing semantic concept tagging of unstructured text, and so realize the Semantic Web. This work presents a novel rule-based approach that uses linguistic grammar-based techniques to extract Arabic composite names from Arabic text. Our approach uniquely exploits the genitive Arabic grammar rules; in particular, the rules regarding the identification of definite nouns (معرفة) and indefinite nouns (نكرة) to support the process of extracting composite names. Furthermore, this approach does not place any constraints on the length of the Arabic composite name. The results of our experiments show that there are improvement in recognizing Arabic composite names entity in the Arabic language text. Our research also contributes a novel, knowledge-based approach to relation extraction from unstructured Arabic text, which is based on the principles of Functional Discourse Grammar (FDG). We further improve the approach by integrating it with Machine Learning relation classification, resulting in a hybrid relation extraction algorithm that can handle especially complex Arabic sentence structures. The accuracy of our relation classification efforts was extensively evaluated by means of experimental evaluation that evidenced the accuracy of the FDG relation extraction approach and the improvement gained by the Machine Learning integration. The essential NLP algorithms of entity recognition and relation extraction were deployed in a Semantic Knowledge-base that was built from the outset to model the knowledge of the problem domain. The semantic modelling of the knowledgebase aided improving the accuracy of the NLP algorithms by leveraging relevant domain knowledge published in Open Linked Datasets. Moreover, the extracted information was semantically tagged and inserted into the Semantic Knowledge-base, which facilitated building advanced rules to infer new interesting information from the extracted knowledge as well as utilising advanced query mechanisms for intelligently exploring the mined problem domain knowledge