Learning Tree Distributions by Hidden Markov Models

Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main approaches in literature, focusing in particular on the causality assumptions introduced by the choice of a specific tree visit direction. We will then sketch a novel non-parametric generalization of the bottom-up hidden tree Markov model with its interpretation as a nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho

Robust Parsing of Spoken Dialogue Using Contextual Knowledge and Recognition Probabilities

In this paper we describe the linguistic processor of a spoken dialogue system. The parser receives a word graph from the recognition module as its input. Its task is to find the best path through the graph. If no complete solution can be found, a robust mechanism for selecting multiple partial results is applied. We show how the information content rate of the results can be improved if the selection is based on an integrated quality score combining word recognition scores and context-dependent semantic predictions. Results of parsing word graphs with and without predictions are reported.Comment: 4 pages, LaTex source, 3 PostScript figures, uses epsf.sty and ETRW.sty, to appear in Proceedings of ESCA Workshop on Spoken Dialogue Systems, Denmark, May 30-June

On the automaticity of language processing

People speak and listen to language all the time. Given this high frequency of use, it is often suggested that at least some aspects of language processing are highly overlearned and therefore occur “automatically”. Here we critically examine this suggestion. We first sketch a framework that views automaticity as a set of interrelated features of mental processes and a matter of degree rather than a single feature that is all-or-none. We then apply this framework to language processing. To do so, we carve up the processes involved in language use according to (a) whether language processing takes place in monologue or dialogue, (b) whether the individual is comprehending or producing language, (c) whether the spoken or written modality is used, and (d) the linguistic processing level at which they occur, that is, phonology, the lexicon, syntax, or conceptual processes. This exercise suggests that while conceptual processes are relatively non-automatic (as is usually assumed), there is also considerable evidence that syntactic and lexical lower-level processes are not fully automatic. We close by discussing entrenchment as a set of mechanisms underlying automatization

Parsing with parallelism : a spreading-activation model of inference processing during text understanding

The past decade of reseatch in Natural Language Processing has universally recognized that, since natural language input is almost always ambiguous with respect to its pragmatic implications, its syntactic parse, and even its lexical analysis (i.e., choice of correct word-sense for an ambiguous word), processing natural language input requires decisions about word meanings, syntactic structure, and pragmatic inferences. The lexical, syntactic, and pragmatic levels of inferencing are not as disparate as they have often been treated in both psychological and artificial intelligence research. In fact, these three levels of analysis interact to form a joint interpretation of text.ATLAST (A Three-level Language Analysis SysTem) is an implemented integration of human language understanding at the lexical, the syntactic, and the pragmatic levels. For psychological validity, ATLAST is based on results of experiments with human subjects. The ATLAST model uses a new architecture which was developed to incorporate three features: spreading activation memory, two-stage syntax, and parallel processing of syntax and semantics. It is also a new framework within which to interpret and tackle unsolved problems through implementation and experimentation