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.

Antigen-dependent induction of a CD40 signal in peripheral B cells

Monoclonal antibodies have emerged as one of the most promising therapeutics in oncology over the last decades. The generation of fully human tumorantigen-specific antibodies suitable for anti-tumor therapy is laborious and difficult to achieve. Autoreactive B cells expressing those antibodies are detectable in cancer patients and represent a suitable source for human antibodies. However, the isolation and cultivation of this cell type is challenging. A novel method was established to identify antigen-specific B cells. The method is based on the conversion of the antigen independent CD40 signal into an antigen-specific one. For that, the artificial fusion proteins ABCos1 and ABCos2 (Antigen-specific B cell co-stimulator) were generated, which consist of an extracellular association-domain derived from the constant region of the human immunoglobulin (Ig) G1, a transmembrane fragment and an intracellular signal transducer domain derived of the cytoplasmic domain of the human CD40 receptor. By the association with endogenous Ig molecules the heterodimeric complex allows the antigen-specific stimulation of both the BCR and CD40. In this work the ability of the ABCos constructs to associate with endogenous IgG molecules was shown. Moreover, crosslinking of ABCos stimulates the activation of NF-κB in HEK293-lucNifty and induces proliferation in B cells. The stimulation of ABCos in transfected B cells results in an activation pattern different from that induced by the conventional CD40 signal. ABCos activated B cells show a mainly IgG isotype specific activation of memory B cells and are characterized by high proliferation and the differentiation into plasma cells. To validate the approach a model system was conducted: B cells were transfected with IVT-RNA encoding for anti-Plac1 B cell receptor (antigen-specific BCR), ABCos or both. The stimulation with the BCR specific Plac1 peptide induces proliferation only in the cotransfected B cell population. Moreover, we tested the method in human IgG+ memory B cells from CMV infected blood donors, in which the stimulation of ABCos transfected B cells with a CMV peptide induces antigen-specific expansion. These findings show that challenging ABCos transfected B cells with a specific antigen results in the activation and expansion of antigen-specific B cells and not only allows the identification but also cultivation of these B cells. The described method will help to identify antigen-specific B cells and can be used to characterize (tumor) autoantigen-specific B cells and allows the generation of fully human antibodies that can be used as diagnostic tool as well as in cancer therapy.