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Hans Uszkoreit PD Dr. Hand-crafted deep linguistic grammars provide precise modeling of human languages, but are deficient in their capability of handling ill-formed or extra-grammatical inputs. In this dissertation, we argue that with a series of robust processing techniques, improved coverage can be achieved without sacrificing efficiency or specificity of deep linguistic processing.

An overview of the robustness problem in state-of-the-art deep linguistic processing systems reveals that insufficient lexicon and overrestricted constructions are the major sources for the lack of robustness.

Targeting both, several robust processing techniques are proposed as add-on modules to the existing deep processing systems. For the lexicon, we propose a deep lexical acquisition model to achieve automatic online detection and acquisition of missing lexical entries. The evaluation shows that our lexical acquisition results significantly improved grammar coverage without noticeable degradation in accuracy. For the constructions, we propose the partial parsing strategy to maximally recover the intermediate results when the full analysis is not available.

Partial parse selection models are proposed and evaluated. Experiment results show that the fragment semantic outputs recovered from the partial parses are of good quality and high value for practical usage.

Also, the efficiency issues are carefully addressed with new extensions to the existing efficient processing algorithms. In dieser Dissertation werden wir zeigen, dass eine verbesserte Abdeckung mit einer Reihe von robusten Verarbeitungstechniken erreicht werden kann, ohne dabei die Effizienz oder auch die Exaktheit tiefer Sprachverarbeitung zu opfern.

Die Auswertung zeigt, dass unsere lexikalische Akquisition eine wesentlich verbesserte Grammatikabdeckung ohne erkennbaren Genauigkeitsverlust erreicht. Es werden partielle Parse-Selektionsmodelle vorgestellt und bewertet.

Versuchsergebnisse zeigen, dass die fragmentierten semantischen Ausgaben, v. Auch werden Effizienzfragen detailliert mittels neuer Erweiterungen zu den bestehenden effizienten Verarbeitungssystemen betrachtet.

I also thank them for providing a wonderful working environment, where I had the chance to meet many others who have contributed to this work. Among them, my sincere thanks go to Dan Flickinger for his continuous support on the grammar, prompt feedback, and inspiring discussions.

I thank Timothy Baldwin, John Carroll, Stephan Oepen, Aline Villavicencio, for sharing ideas, helping with experiments, and providing healthy criticisms. My thanks also go to Valia Kordoni, Rebecca Dridan, Jeremy Nicholson, who have helped to proofread the dissertation and generously offered numerous corrections and suggestions. Of course, any remaining errors are mine. A final word of gratitude is for my mother, Heping Li, and my wife, Yu Chen, for their unwavering support, without which I would not have been willing or able to find my place in this profession.

Does Precision Matter? Robert Browning The ideas in this dissertation grew out of my experience with grammar development and my attempts at building applications based on such grammars. Back then, I was not familiar with large scale linguistically motivated grammars, and was instantly fascinated by how linguistic studies can be formally described and implemented, and the potential applications of such promising language resources. This was partly because there was no large scale HPSG grammar for Chinese at that time, but another more practical reason was to familiarize myself with the deep processing tools.

After struggling through months of frustration, I managed to construct a sketch of a small grammar that covers the basic constructions. By then I realized that writing a grammar of reasonable size would probably take years, if not decades, particularly because there is a lack of systematic theoretical study of HPSG for Chinese specific language phenomena.

In a retrospect at this point, I found myself to be nowhere near my initial intention of building a language resource that can be useful for applications. The doubt led to a quick retreat. Losing certainty about deep linguistic processing in general, I looked for comfort from existing large grammars.

It did not take me long to realize that even with the largest grammars that represent the state 1. Introduction of the art grammar engineering results, various problems exist when one tries to use them in real applications. It is not just a coincidence that deep linguistic processing has been a disfavored approach for a long time.

The most prominent problem among others is a lack of robustness. It occurred to me that it would be a more interesting topic for me to search for solutions to this problem. Following this thread, I have been working on robust deep processing techniques since that time, and most of the work made its way into this dissertation.

This dissertation describes a series of techniques that lead towards robust deep linguistic processing. In this chapter, I will define the concept of deep linguistic processing, followed by an overview of the state of the art deep linguistic processing platforms, as well as the main challenges it is faced with.

Finally, the structure of the dissertation is outlined at the end of the chapter. Such approaches are related to either a particular linguistic theory e. Traditionally, deep linguistic processing has been concerned with grammar development for parsing and generation, with many deep processing systems using the same grammar for both directions.

Being grammar centric, the studies of deep linguistic processing mainly focus on two questions: How to develop linguistically motivated deep grammars? How to effectively utilize the knowledge in the given deep grammars to achieve the application tasks? The first question leads to a whole sub-field of study in grammar engineering, while the second question is closely related to process-.

Grammar Grammar is the study of rules governing the use of language. Systematic studies of grammars started thousands of years ago and the methodology has been constantly evolving over the time. Since the s, a new branch of language study named computational linguistics, has emerged as a new field and opened up several novel ways of grammar study.

Among them, the approach to describe natural language with formal grammars has been widely attended, with both fruitful success and miserable setbacks. Formal grammar is an abstract structure that describes a formal language precisely. Though doubts as to whether formal grammar is capable of describing human languages have always been around, it has never impeded the ambitious attempts of building large-scale formal grammars for various human languages.

More recent approaches aim at both broad coverage and high accuracy of languages without domain constraints, for both parsing and generation tasks.

While the development of grammars were taking place, researchers soon realized that the growth of the grammars heavily depends on the description language of the grammar, the formalism framework. The quest for a better, more powerful, while computationally affordable framework soon branched into various grammar formalisms. The choice of different grammar formalisms leads to the later blossom of various linguistic theories: transformational-generative grammar TGG , categorial grammar CG , dependency grammar DG , treeadjoining grammar TAG , lexical functional grammar LFG , generalized phrase structure grammar GPSG , head-driven phrase structure grammar HPSG , just to name a few of them.

Despite the differences among different frameworks, grammar development is almost always a painstaking task. Especially when aim-. Introduction ing for both broad-coverage and high precision, it usually takes years if not decades before the grammar can reach a reasonable size. Also, due to the strong cohesion in language phenomena, a slight change of the grammar in one aspect might result in dramatic changes in other corners.

This makes it hard to modularize the task of grammar development. Large grammars are typically written by very few linguists continuously over decades. Distributed parallel grammar development is very difficult in practice, if possible, at all. Nevertheless, the continuous work on grammar engineering has seen fruitful outcomes in recent years.

Details about the latest achievements in grammar engineering will be discussed in Section 1. It is worth noting that another relatively new approach of grammar development has emerged in recent years. Instead of hand-crafting the grammar, the approach extracts or induces the grammar from annotated corpora i.

In such an approach, the main effort shifts to the creation of large amounts of annotated data. This is achieved by either setting up a good annotation guideline and employing multiple human annotators, or semi-automatically converting the existing treebanks into annotations that are compatible with the underlying grammar framework and include richer linguistic information. The grammars created by such methods usually take shorter development time and the performance of the grammar can be fine-tuned by either expanding the treebank or by improving the extraction algorithm.

The main problem with this approach is that the grammars are usually less accurate from two aspects. First, the depth of the grammar is largely dependent on the annotation. Asking human annotators to label detailed linguistic information on the treebank is very difficult, and will inevitably lead to low inter-annotator agreement.

The semi-automatic conversion approach requires the existence of multiple linguistic resources, and their inter-compatibility. Second, the treebank induced grammars usually overgenerate massively.

It is typically the case that only grammatically well-formed sentences are annotated in the treebank. Therefore, the induced. And the resulting grammar produces a huge amount of analyses per input, not all of which are correct. For parsing tasks, the correct analysis is selected by a parse disambiguation model. But such grammars are less suitable for generation tasks.

In this dissertation, we focus on the deep linguistic processing techniques that rely on hand-crafted deep grammars, simply because they are such distinct grammar resources that provide accurate modeling of human languages Processing Given a grammar, either hand-crafted or treebank induced, it requires extra processing techniques to utilize the encoded linguistic knowledge.

Typically, there are two types of tasks in which the grammar is used: parsing and generation. The parsing task is concerned with converting natural language strings to linguistically annotated outputs. In deep linguistic parsing, the output contains not only basic syntactic information, but often semantic analysis, as well.

The exact output annotation varies a lot depending on the framework, but they normally share the properties of exploring a huge solution space. This requires various efficient processing techniques to facilitate the search for either exact or approximate best results. In the generation task, the processing goes in the opposite direction. The output of the processing is the natural language utterances, while the input is the abstract semantic representation of the meaning. Similar efficiency and specificity challenges exist for generation tasks, but now the disambiguation model needs to select the best natural language utterances.

It should be noted that the processing techniques in use for a specific task are largely dependent on the characteristics of the grammar. For grammars aiming at high precision, the coverage is usually low, hence robust processing techniques are necessary. For grammars aiming at broad-coverage, overgeneration is often a problem, therefore. Introduction a more sophisticated disambiguation step is of higher importance.

For grammars that aim at both, a mixture of different techniques is needed to achieve a balanced performance.

It should also be noted that, even with the same grammar, when used in different application tasks, different configurations of the processing modules should be used to achieve optimal functionality Deep vs. Shallow The term deep linguistic processing intends to differentiate the strongly linguistic theory-driven processing techniques we have discussed from those approaches which are less linguistically driven.

The latter class of approaches are referred to as shallow processing techniques, for they usually concentrate on specific language phenomena or application tasks without thoroughly modeling of the language.

By this definition, processing techniques like part of speech tagging, named entity recognition, and phrase chunking all belong to shallow processing. However, it should be pointed out that there is no absolute boundary between deep and shallow processing techniques. Rather, the terms deep and shallow should be taken in a relative sense.


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