This paper presents a semiautomatic technique for developing broad-coverage finite-state morphological analyzers for use in natural language processing applications. It consists of three components—elicitation of linguistic information from humans, a machine learning bootstrapping scheme, and a testing environment. The three components are applied iteratively until a threshold of output quality is attained. The initial application of this technique is for the morphology of low-density languages in the context of the Expedition project at NMSU Computing Research Laboratory. This elicit-build-test technique compiles lexical and inØectional information elicited from a human into a finite-state transducer lexicon and combines this with a sequence of morphographemic rewrite rules that is induced using transformation-based learning from the elicited examples. The resulting morphological analyzer is then tested against a test set, and any corrections are fed back into the learning procedure, which then builds an improved analyzer.

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Núñez et al.'s (2019) negative assessment of the field of cognitive science derives from evaluation criteria that fail to reflect the true nature of the field. In reality, the field is thriving on both the research and educational fronts, and it shows great promise for the future.

This paper describes the initial results of an experiment in integrating knowledge-based text processing with real-world reasoning in a question answering system. Our MOQA "meaning-oriented question answering" system seeks answers to questions not in open text but rather in a structured fact repository whose elements are instances of ontological concepts extracted from the text meaning representations (TMRs) produced by the OntoSem text analyzer. The query interpretation and answer content formulation modules of MOQA use the same knowledge representation substrate and the same static knowledge resources as the ontological semantic (OntoSem) semantic text analyzer. The same analyzer is used for deriving the meaning of questions and of texts from which the fact repository content is extracted. Inference processes in question answering rely on ontological scripts (complex events) that also support reasoning for purely NLP-related purposes, such as ambiguity resolution in its many guises.

This paper describes a semantically rich, human-aided machine annotation system created within the Ontological Semantics (OntoSem) environment using the DEKADE toolset. In contrast to mainstream annotation efforts, this method of annotation provides more information at a lower cost and, for the most part, shifts the maintenance of consistency to the system itself. In addition, each tagging effort not only produces knowledge resources for that corpus, but also leads to improvements in the knowledge environment that will better support subsequent tagging efforts.