This paper describes an early step in approaching implicit meaning computationally. It outlines various types of implicit meanings and then presents a method of finding the so called defaults - omissions that are universally reconstructable and, of course, interpretible without much additional reasoning. The defaults are analyzed on the example of the 1000 instances of TerminateLife events, and a small illustrative experiment is described, where defaults of the event and its children are computationally recovered.

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 recent years, especially with the backing of HIPPA, anonymization of records became an increasing concern. Much research has been done in the area of anonymization of medical records. Anonymization is often based on generalizing, or removing, several fields which have been deemed identifiable. Little research has been done on anonymizing patient symptoms themselves. In this paper, we propose an algorithm for anonymizing patient symptom data, for the use by researchers, using a semantically-driven ontology and methodology known as Ontological Semantic Technology.

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.

The paper makes a case for expanding the range of words that Computing With Words typically considers to, eventually, all the words in a natural language, thus accounting accurately for the inherent vagueness of natural language meaning and creating an overlap with computational semantics. The claim is illustrated with examples of a few English nouns and verbs rather than the usual adjectives and their close derivatives.

Firefighters put themselves in harm's way while saving others and may even lose their lives in certain situations, such as toxic fumes, extreme heat, or inhaling smoke. In order to protect firefighters from the risks and, at the same time, to save others' lives, firefighting and firefighter assistant robots have been developed. This paper will compare different firefighting and firefighter assistant robots and their functionalities. The main thrust of the paper, however, is to discuss the transition from tele-operated robots first to voice-operated and, eventually, to fully autonomous ones, which is where robotic intelligence resides. We will introduce HARMS, the human-agent-robot-machine-sensor collaborative effort, and explain why using natural language as the basis of their communication is not only optimal but also feasible and affordable with the Ontological Semantic Technology.

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.

Our premise is that an intelligent system should be able to structure all the information that can be obtained from natural language text, and it should do it in such a manner that the structured information be useful for further processing. This paper presents an experiment in structuring information from the natural language incomplete descriptions of 101 animals collected from a children's dictionary. The goal of this experiment is to use computational semantic analysis of the natural language descriptions-which are quite heavy in common sense knowledge-and to come up with a hierarchy of and similarities among the described animals, as well as flagging descriptions that are largely inconsistent with other information.

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.

This paper describes the process of deriving the meaning of an unknown word within the framework of meaning based natural language processing. It uses the clues supplied by the rest of the sentence, taking into account various degrees of possibilities of what the unknown word can mean, according to the previously acquired knowledge resources. The process of finding the meaning is incremental, and thus the derived meaning can evolve as more knowledge is gathered. The paper describes the fuzzy approach to the problem within this framework with regard to the semantic and syntactic acceptability of a sentence.

The paper describes a semi-automatic method of identifying common-sense knowledge by running an ontological semantic system and focusing on its failures to interpret sentences that a human is not challenged by. Without common-sense knowledge, "He put a banana in his trunk" produces 6 representations, corresponding to 6 senses of "trunk" recorded in the lexicon and anchored in different ontological concepts - roughly, car-part, elephant-part, tree-part, torso, luggage, software-term. A human language user reduces them to 3, using several pieces of common-sense knowledge that should therefore be added to the system resources to improve its performance. The significant result is that by running the system not only experimentally but also in real-life applications, we ensure an ongoing test against the lack of common-sense knowledge, at least some of which is known to be captured by the system, if it interprets the text correctly and not captured when the interpretation is inadequate.

This paper describes a computational system for detecting unintentional inferences in casual unsolicited and unrestricted verbal output of individuals, potentially responsible for leaked classified information to people with unauthorized access. Uses of the system for cases of insider threat and/or social engineering are discussed. Brief introductions to Ontological Semantic Technology and Natural Language Information Assurance and Security are included.

A recent Facebook update informed a user's friends that, on a night out at a bar, “a white dude was hitting on me all night”. It occurred to a couple of us independently that the user was not white or at least very likely not to be white, which was actually confirmed by those in the know. This paper is an attempt to explore and explain, the nature of this and similar inferences and to sketch out a way to compute them. A thorough search by a noted pragmaticist, in our ranks, has failed to produce any similar study, and it is borne out by the map and trends in the field. We have also realized that the inference can serve as a new and more rigorous explanation for a large class of jokes that none of us, humor researchers all (among other things), has ever seen analyzed that way. The largely formalism-free façade of the paper should not distract a reader from the technical nature of the argument.

This paper explores delegation decisions predicated on models of trust and autonomy among agents. In socially rich environments, trust and autonomy of artificial agents are key attributes for rational delegation decisions. Social agents are affected

An overview of a model for computational recognition of short jokes that are based on phonological similarity of words is presented. The jokes are taken from joke books. The model takes into account orthographic, phonological and semantic representation of words. A motivation for using this model, as well as steps for a joke recognizer, are discussed based on an example. The joke recognition is based on knowledge that is provided by an ontology. The ontology is created using entries from a children's dictionary. Words in the dictionary are instances of concepts in the ontology. Semantic relationships between concepts are added from a collection of children's texts and definitions in a children's dictionary. Any short text is considered a joke if it contains two scripts that both overlap and oppose; and if there is a pair of similar sounding words, in which the first word is an instance of a concept of the first script, and the second word is an instance of a concept of the second script.