Automatic induction of lexical features

This thesis concerns artificially intelligent natural language processing systems that are capable of learning the properties of lexical items (properties like verbal valency or inflectional class membership) autonomously while they are fulfilling their tasks for which they have been deployed in the first place. Many of these tasks require a deep analysis of language input, which can be characterized as a mapping of utterances in a given input C to a set S of linguistically motivated structures with the help of linguistic information encoded in a grammar G and a lexicon L: G + L + C → S (1) The idea that underlies intelligent lexical acquisition systems is to modify this schematic formula in such a way that the system is able to exploit the information encoded in S to create a new, improved version of the lexicon: G + L + S → L' (2) Moreover, the thesis claims that a system can only be considered intelligent if it does not just make maximum usage of the learning opportunities in C, but if it is also able to revise falsely acquired lexical knowledge. So, one of the central elements in this work is the formulation of a couple of criteria for intelligent lexical acquisition systems subsumed under one paradigm: the Learn-Alpha design rule. The thesis describes the design and quality of a prototype for such a system, whose acquisition components have been developed from scratch and built on top of one of the state-of-the-art Head-driven Phrase Structure Grammar (HPSG) processing systems. The quality of this prototype is investigated in a series of experiments, in which the system is fed with extracts of a large English corpus. While the idea of using machine-readable language input to automatically acquire lexical knowledge is not new, we are not aware of a system that fulfills Learn-Alpha and is able to deal with large corpora. To instance four major challenges of constructing such a system, it should be mentioned that a) the high number of possible structural descriptions caused by highly underspeci ed lexical entries demands for a parser with a very effective ambiguity management system, b) the automatic construction of concise lexical entries out of a bulk of observed lexical facts requires a special technique of data alignment, c) the reliability of these entries depends on the system's decision on whether it has seen 'enough' input and d) general properties of language might render some lexical features indeterminable if the system tries to acquire them with a too high precision. The cornerstone of this dissertation is the motivation and development of a general theory of automatic lexical acquisition that is applicable to every language and independent of any particular theory of grammar or lexicon. This work is divided into five chapters. The introductory chapter first contrasts three different and mutually incompatible approaches to (artificial) lexical acquisition: cue-based queries, head-lexicalized probabilistic context free grammars and learning by unification. Then the postulation of the Learn-Alpha design rule is presented. The second chapter outlines the theory that underlies Learn-Alpha and exposes all the related notions and concepts required for a proper understanding of artificial lexical acquisition. Chapter 3 develops the prototyped acquisition method, called ANALYZE-LEARN-REDUCE, a framework which implements Learn-Alpha. The fourth chapter presents the design and results of a bootstrapping experiment conducted on this prototype: lexeme detection, learning of verbal valency, categorization into nominal count/mass classes, selection of prepositions and sentential complements, among others. The thesis concludes with a review of the conclusions and motivation for further improvements as well as proposals for future research on the automatic induction of lexical features.

Thermal excitations of optical nanofibers measured with a fiber-integrated Fabry-Pérot cavity

Efficient coupling of light to quantum emitters, such as atoms, molecules or quantum dots, is one of the great challenges in current research. The interaction can be strongly enhanced by coupling the emitter to the eva-nescent field of subwavelength dielectric waveguides that offer strong lateral confinement of the guided light. In this context subwavelength diameter optical nanofibers as part of a tapered optical fiber (TOF) have proven to be powerful tool which also provide an efficient transfer of the light from the interaction region to an optical bus, that is to say, from the nanofiber to an optical fiber. rnAnother approach towards enhancing light–matter interaction is to employ an optical resonator in which the light is circulating and thus passes the emitters many times. Here, both approaches are combined by experi-mentally realizing a microresonator with an integrated nanofiber waist. This is achieved by building a fiber-integrated Fabry-Pérot type resonator from two fiber Bragg grating mirrors with a stop-band near the cesium D2-line wavelength. The characteristics of this resonator fulfill the requirements of nonlinear optics, optical sensing, and cavity quantum electrodynamics in the strong-coupling regime. Together with its advantageous features, such as a constant high coupling strength over a large volume, tunability, high transmission outside the mirror stop band, and a monolithic design, this resonator is a promising tool for experiments with nanofiber-coupled atomic ensembles in the strong-coupling regime. rnThe resonator's high sensitivity to the optical properties of the nanofiber provides a probe for changes of phys-ical parameters that affect the guided optical mode, e.g., the temperature via the thermo-optic effect of silica. Utilizing this detection scheme, the thermalization dynamics due to far-field heat radiation of a nanofiber is studied over a large temperature range. This investigation provides, for the first time, a measurement of the total radiated power of an object with a diameter smaller than all absorption lengths in the thermal spectrum at the level of a single object of deterministic shape and material. The results show excellent agreement with an ab initio thermodynamic model that considers heat radiation as a volumetric effect and that takes the emitter shape and size relative to the emission wavelength into account. Modeling and investigating the thermalization of microscopic objects with arbitrary shape from first principles is of fundamental interest and has important applications, such as heat management in nano-devices or radiative forcing of aerosols in Earth's climate system. rnUsing a similar method, the effect of the TOF's mechanical modes on the polarization and phase of the fiber-guided light is studied. The measurement results show that in typical TOFs these quantities exhibit high-frequency thermal fluctuations. They originate from high-Q torsional oscillations that couple to the nanofiber-guided light via the strain-optic effect. An ab-initio opto-mechanical model of the TOF is developed that provides an accurate quantitative prediction for the mode spectrum and the mechanically induced polarization and phase fluctuations. These high-frequency fluctuations may limit the ultimate ideality of fiber-coupling into photonic structures. Furthermore, first estimations show that they may currently limit the storage time of nanofiber-based atom traps. The model, on the other hand, provides a method to design TOFs with tailored mechanical properties in order to meet experimental requirements. rn