German has a productive morphology and allows the creation of complex words which are often highly ambiguous. This paper reports on the development of a head-lexicalized PCFG for the disambiguation of German morphological analyses. The grammar is trained on unlabeled data using the Inside-Outside algorithm. The parser achieves a precision of more than 68% on difficult test data, which is 23% mo...

This paper proposes a modelization of the coordination within the framework of Head-driven Phrase Structure Grammar (HPSG), based on concepts of functor, arguments and subcategorization requirements. It enables coordination of more than one constituent as well as of different categories. This modelization lies on a lexicalization of the coordination principle and two generalizations of the noti...

We present a new approach to stochastic modeling of constraintbased grammars that is based on loglinear models and uses EM for estimation from unannotated data. The techniques are applied to an LFG grammar for German. Evaluation on an exact match task yields 86% precision for an ambiguity rate of 5.4, and 90% precision on a subcat frame match for an ambiguity rate of 25. Experimental comparison...

In this paper, we show that by integrating existing NLP techniques and Semantic Web tools in a novel way, we can provide a valuable contribution to the solution of the knowledge acquisition bottleneck problem. NLP techniques to create a domain ontology on the basis of an open domain corpus have been combined with Semantic Web tools. More specifically, Watson and Prompt have been employed to enh...

This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and provides a mechanism for parse selection by means of statistical inference. Unlike probabilistic contextfree grammars, it is a context-sensitive grammar formalism and it has the abil...

The behavioral specification of an object-oriented grammar model is considered. The model is based on full lexicalization, head-orientation via valency constraints and dependency relations, inheritance as a means for non-redundant lexicon specification, and concurrency of computation. The computation model relies upon the actor paradigm, with concurrency entering through asynchronous message pa...

We argue that two types of context are central to grounding the semantics for the mass/count distinction. We combine and develop the accounts of Rothstein (2010) and Landman (2011), which emphasize (non-)overlap at a context. We also adopt some parts of Chierchia’s (2010) account which uses precisifying contexts. We unite these strands in a two-dimensional semantics that covers a wide range of ...

In this article, we give an overview of Natural Language Generation (nlg) from an applied system-building perspective. The article includes a discussion of when nlg techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic nlg tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generat...

Linked Data has emerged as the most widely used and the most powerful knowledge source for Question Answering (QA). Although Question Answering using Linked Data (QALD) fills in many gaps in the traditional QA models, the answers are still presented as factoids. This research introduces an answer presentation model for QALD by employing Natural Language Generation (NLG) to generate natural lang...

We present an Integer Linear Programming model of content selection, lexicalization, and aggregation that we developed for a system that generates texts from OWL ontologies. Unlike pipeline architectures, our model jointly considers the available choices in these three text generation stages, to avoid greedy decisions and produce more compact texts. Experiments with two ontologies confirm that ...