The paper addresses the phenomenon of direct object defaults in text as part of exploring the meaning of the unsaid and making it accessible for computer understanding. It describes a large but reasonably simple computer experiment on the basis of one hypothesis about defaults, namely, that a true default may appear as a direct object only if modified, e.g., Bob ate fresh food but? Bob ate food. The algorithm reduced over 24,000 occurrences, in Wikipedia for Schools, of the 200 most frequent verbs with and without direct objects, and those direct objects with and without modifiers, to over 200,000 default candidates. A discussion dealt with ways of restricting this list further to actual defaults.

This paper compares the results of computer and human efforts to determine whether an email is legitimate or a phishing attempt. For this purpose, we have run two series of experiments, one for the computer and the other for human subjects. Both experiments addressed the same corpora, one of phishing emails, and the other of legitimate ones. Both the computer and human subjects were asked to detect which emails were phishing and which were legitimate. The results are interesting, both separately and in comparison. Even at this limited, non-semantic state of computation, they indicate that human and computer competences should complement each other, and that, of course, will lead to the integration of human-accessible semantics into computation.

In addressing a verb bias in syntactic analysis we present the beginnings of a noun-driven analysis paradigm. Such a paradigm may complement and compensate for some weaknesses in the existing verb-driven paradigm (in which the noun is subordinated) as applied to information sources or contexts in which the data is structured in objects and more than in events.

Stylometry is the quantified (often statistical) analysis of author style as a set of (usually morphosyntactic) features expressed in several documents by the author. The focus of this paper is a task to which stylometry is often applied: authorship attribution, the question of identifying or confirming the author of a text based on the known body of work. We analyze a feature set previously introduced in the field, using a tool and corpus already available. Decomposing the set, we identify the features that seem to have contributed the most to accurate performance. In re-composing the set under different objectives - first, for English-only document sets, and then for possible multi-language use - we identify smaller sets of feature combinations that work well together in accurate performance. We then outline our continuing work based on the results we obtain.

Stylometry is the measurement of certain expressible features of writing style, and its uses include the characterization of authors for recognition in cases of text whose authorship is disputed or unknown. This work builds upon previous investigation into the success of a particular feature set on a particular corpus. We explore the creation and testing of a small corpus spanning multiple languages and character sets, and the building of a feature set for use in author attribution problems over that corpus. In-depth analysis of results is used to motivate further work.

Natural language understanding systems are increasingly needed for intuitive, efficient interaction with large information stores. The object-centered nature of these stores - that they encode states, attributes, and relationships of objects - may not be best served by the current verb-driven syntactic paradigm. We develop a highly-parallelizable noun-driven syntax in response, and evaluate its performance against that of other syntactic paradigms through a series of search queries rooted in basic processing operations.

This paper revises the classic Ontological Semantics theory with regard to the output of the analyzer. We argue that it is not enough to produce semantic interpretation of text, and syntactic trees should serve not only as clues for semantic processing but also as an output in its own right. We show that it is useful to combine both results of syntactic and semantic processing in a single output while at the same time maintaining its own unique pieces on testbed of several sentences containing complex nominals and compare these results to interpretations of human subjects.