Yochai Cohen

A Novel Procedure for Measuring Semantic Synergy

One interesting characteristic of some complex systems is the formation of macro level constructions perceived as having features that cannot be reduced to their micro level constituents. This characteristic is considered to be the expression of synergy where the joint action of the constituents produces unique features that are irreducible to the constituents isolated behavior or their simple composition. The synergy, characterizing complex systems, has been well acknowledged but difficult to conceptualize and quantify in the context of computing the emerging meaning of various linguistic and conceptual constructs. In this paper, we propose a novel measure/procedure for quantifying semantic synergy. This measure draws on a general idea of synergy as has been proposed in biology. We validate this measure by providing evidence for its ability to predict the semantic transparency of linguistic compounds (Experiment 1) and the abstractness rating of nouns (Experiment 2).

Profiling School Shooters: Automatic Text-Based Analysis

School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various characteristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by 6 school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters’ texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/prioritization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology.

Opposition theory and computational semiotics

Opposition theory suggests that binary oppositions (e.g., high vs. low) underlie basic cognitive and linguistic processes. However, opposition theory has never been implemented in a computational cognitive-semiotics model. In this paper, we present a simple model of metaphor identification that relies on opposition theory. An algorithm instantiating the model has been tested on a data set of 100 phrases comprising adjective-noun pairs in which approximately a half represent metaphorical language-use (e.g., dark thoughts) and the rest literal language-use (e.g., dark hair). The algorithm achieved 89% accuracy in metaphor identification and illustrates the relevance of opposition theory for modelling metaphor processing.

How do we understand the meaning of connotations? A cognitive computational model

Abstract Denotation is the literal sense of a word, while connotation is its extended sense. The current paper presents a cognitive computational model of the adjective’s connotation (e.g., sweet baby). We tested the model by developing a novel algorithm – ConnoSense – that identifies the sense of an attribute’s connotation. More specifically, ConnoSense identifies the sense of an attribute such as in the case of a “sweet smile” where the attribute/adjective “sweet” is used in the sense of “friendly.” Tested on a multiple-choice test of identifying the sense of a connotation (e.g., “dark thoughts”) the algorithm gained 90% accuracy and outperforms two other models that are based on vectorial semantics. These results support the validity of our model. The paper points at the importance of fusing ideas from the semiotics interpretative tradition with experimental psychological knowledge and novel methodologies of computational semantics.

Is the whole different from the sum of its parts? A proposed procedure for measuring divergence from additivity

ABSTRACT The behaviour of many small systems, such as a family or a football team, epitomizes the famous Gestalt slogan of a “Whole which is different from the sum of its parts”. However, and in the context of these small cognitive systems, conceptualizing and measuring the extent and the way in which a whole is different from the sum of its parts is a non-trivial challenge. One possible direction for addressing this challenge involves measuring the extent in which the entropy of the whole is non-additive, meaning different from the sum of the entropy of its parts. However, measuring divergence from additivity is far from trivial. In this short paper, we propose a simple procedure for measuring divergence from additivity and illustrate the procedure by analysing the behaviour of a soccer team.

The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis

To optimize its performance, a competitive team, such as a soccer team, must maintain a delicate balance between organization and disorganization. On the one hand, the team should maintain organized patterns of behavior to maximize the cooperation between its members. On the other hand, the team’s behavior should be disordered enough to mislead its opponent and to maintain enough degrees of freedom. In this paper, we have analyzed this dynamic in the context of soccer games and examined whether it is correlated with the team’s performance. We measured the organization associated with the behavior of a soccer team through the Tsallis entropy of ball passes between the players. Analyzing data taken from the English Premier League (2015/2016), we show that the team’s position at the end of the season is correlated with the team’s entropy as measured with a super-additive entropy index. Moreover, the entropy score of a team significantly contributes to the prediction of the team’s position at the end of the season beyond the prediction gained by the team’s position at the end of the previous season.

Identifying the Meta-Forms of Situations: A Case-Study in Computational Semiotics

Various tasksofcomputingmeaning involve the identification, representationandprocessingof interconnectedandrecurrentpatternsknownassituations,context,framesorforms.Inthispaper, the authors propose a novel computational - semiotics approach for addressing various tasks of situationsemantics.Theapproachreliesonthedual-spacemodelandtherepresentationofasituation basedondomainandfunctionsimilarityofitsconstitutingparts.Theauthorsillustratethisapproach throughaworked-outexampleandtestitbyautomaticallyby(1)Judgingthetruth-valueofsituational propositionsand(2)Generatingexplanationstometaphors. KeywoRdS Cognition and Language, Computational Semiotics, Interdisciplinary Research, Meta-Form, Situation

A Novel Methodology for Automatically Measuring Psychological Dimensions in Textual Data

Context. In Proc. EMNLP, Edinburgh, UK, July 27–29, pp. 680–690. Association for Computational Linguistics, Stroudsburg, PA. [24] Turney, P.D. (2012) Domain and function: a dual-space model of semantic relations and compositions. J. Artif. Intell. Res., 44,