A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge

The purpose of this work is to investigate the process of grammatical acquisition from data. In order to do that, a computational learning system is used, composed of a Universal Grammar with associated parameters, and a learning algorithm, following the Principles and Parameters Theory. The Universal Grammar is implemented as a Unification-Based Generalised Categorial Grammar, embedded in a default inheritance network of lexical types. The learning algorithm receives input from a corpus of spontaneous child-directed transcribed speech annotated with logical forms and sets the parameters based on this input. This framework is used as a basis to investigate several aspects of language acquisition. In this thesis I concentrate on the acquisition of subcategorisation frames and word order information, from data. The data to which the learner is exposed can be noisy and ambiguous, and I investigate how these factors a#ect the learning process. The results obtained show a robust learner converging towards the target grammar given the input data available. They also show how the amount of noise present in the input data a#ects the speed of convergence of the learner towards the target grammar. Future work is suggested for investigating the developmental stages of language acquisition as predicted by the learning model, with a thorough comparison with the developmental stages of a child. This is primarily a cognitive computational model of language learning that can be used to investigate and gain a better understanding of human language acquisition, and can potentially be relevant to the development of more adaptive NLP technology.

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1.1 The range of focus phenomena.................... 3

Answer Extraction (AE) systems retrieve phrases in textual documents that directly answer natural language questions. AE over technical m...

This paper pursues some of the consequences of the idea that there are (at least) two sources for distributive/cumulative interpretations in English. One source is lexical pluralization: All predicative stems are born as plurals, as Manfred Krifka and Fred Landman have argued. Lexical pluralization should be available in any language and should not depend on the particular make-up of its DPs. I suggest that the other source of cumulative/distributive interpretations in English is directly provided by plural DPs. DPs with plural agreement features can ‘release ’ those features to pluralize adjacent verbal projections. If there is a lexical source for distributive/cumulative interpretations, there should be instances of such interpretations with singular DPs. But there should also be cases of distributive/cumulative interpretations that require the presence of DPs with plural agreement morphology. What is the role of events in all of this? Events have played a major role in the semantics of plurality since the pioneering work of Barry Schein and Peter Lasersohn. Yet to the present day, there is no consensus about the need of event-based accounts of plurality. Non-eventbased analyses of plural phenomena continue to be proposed. The phenomena discussed in this paper all present small or not so small conceptual problems for event-less analyses, but can be given elegant accounts within frameworks that incorporate some version of a Davidsonian event semantics. The hope is, then, that an event semantics for plurals might at least be a good bet about reality.

Abstract: This article argues that the mood morphemes found on punctual verbs in Mohawk are to be analyzed semantically as markers of verbal definiteness. In particular, the so-called future marker is actually an indefinite morpheme indicating that the event argument of the verb undergoes Heim’s (1982) rule of Quantifier Indexing. In contrast, the seeming past marker is a definite morpheme, indicating that the event argument is immune to Quantifier Indexing. This explains many apparent peculiarities of the Mohawk verbal system, including the use of “future ” as a past habitual form, the use of mood in conditionals, free relatives, and complement clauses, and the incompatibility of “past ” and negation. The relationship between indefinite mood and future events, where it exists, is explicated in terms of an observation by Kamp and Reyle (1993) concerning how humans conceive of the future as different from the past.

Abstract. TimeML is a specification language for the annotation of events and temporal expressions in natural language text. In addition, the language introduces three relational tags linking temporal objects and events to one another. These links impose both aspectual and temporal ordering over time objects, as well as mark up subordination contexts introduced by modality, evidentiality, and factivity. Given the richness of this specification, the TimeML working group decided not to include the arguments of events within the language specification itself. Full reasoning and inference over natural language texts clearly requires knowledge of events along with their participants. In this paper, we define the appropriate role of argumenthood within event markup and propose that TimeML should make a basic distinction between arguments that are events and those that are entities. We first review how TimeML treats event arguments in subordinating and aspectual contexts, creating event-event relations between predicate and argument. As it turns out, these constructions cover a large number of the argument types selected for by event predicates. We suggest that TimeML be enriched slightly to include causal predicates, such as lead to, since these also involve event-event relations. We propose that all other verbal arguments be ignored by the specification, and any predicate-argument binding of participants to an event should be performed by independent means. In fact, except for the event-denoting arguments handled by the extension to TimeML proposed here, almost full temporal ordering of the events in a text can be computed without argument identification.

We present a method for training a statistical model for mapping natural language sentences to semantic expressions. The semantics are expressions of an underspecified logical form that has properties making it particularly suitable for statistical mapping from text. An encoding of the semantic expressions into dependency trees with automatically generated labels allows application of existing methods for statistical dependency parsing to the mapping task (without the need for separate traditional dependency labels or parts of speech). The encoding also results in a natural per-word semantic-mapping accuracy measure. We report on the results of training and testing statistical models for mapping sentences of the Penn Treebank into the semantic expressions, for which per-word semantic mapping accuracy ranges between 79 % and 86 % depending on the experimental conditions. The particular choice of algorithms used also means that our trained mapping is deterministic (in the sense of deterministic parsing), paving the way for large-scale text-to-semantic mapping. 1

The theory of the Theta System (Reinhart 2000, 2002) gives a compositional account of verbal argument structure and argument structure alternations. Although it makes concrete predictions with respect to argument projection and syntax, Reinhart has not provided a definition of the semantics, and semantic operations, associated with the system’s primitive elements and operators. In this paper I provide a