Memory-Based Lexical Acquisition and Processing

Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page

The Many Functions of Discourse Particles: A Computational Model of Pragmatic Interpretation

We present a connectionist model for the interpretation of discourse\ud particles in real dialogues that is based on neuronal\ud principles of categorization (categorical perception, prototype\ud formation, contextual interpretation). It can be shown that\ud discourse particles operate just like other morphological and\ud lexical items with respect to interpretation processes. The description\ud proposed locates discourse particles in an elaborate\ud model of communication which incorporates many different\ud aspects of the communicative situation. We therefore also\ud attempt to explore the content of the category discourse particle.\ud We present a detailed analysis of the meaning assignment\ud problem and show that 80%– 90% correctness for unseen discourse\ud particles can be reached with the feature analysis provided.\ud Furthermore, we show that ‘analogical transfer’ from\ud one discourse particle to another is facilitated if prototypes\ud are computed and used as the basis for generalization. We\ud conclude that the interpretation processes which are a part of\ud the human cognitive system are very similar with respect to\ud different linguistic items. However, the analysis of discourse\ud particles shows clearly that any explanatory theory of language\ud needs to incorporate a theory of communication processes

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Morphological Analysis as Classification: an Inductive-Learning Approach

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm IB1-IG performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages over the traditional approach of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP proceedings style nemlap.sty; inputs ipamacs (international phonetic alphabet) and epsf macro