Tag Archives: Similarity

Good review of similarity measures between elements with semantics

Mohammad Taher Pilehvar, Roberto Navigli, From senses to texts: An all-in-one graph-based approach for measuring semantic similarity, Artificial Intelligence, Volume 228, November 2015, Pages 95-128, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.07.005.

Quantifying semantic similarity between linguistic items lies at the core of many applications in Natural Language Processing and Artificial Intelligence. It has therefore received a considerable amount of research interest, which in its turn has led to a wide range of approaches for measuring semantic similarity. However, these measures are usually limited to handling specific types of linguistic item, e.g., single word senses or entire sentences. Hence, for a downstream application to handle various types of input, multiple measures of semantic similarity are needed, measures that often use different internal representations or have different output scales. In this article we present a unified graph-based approach for measuring semantic similarity which enables effective comparison of linguistic items at multiple levels, from word senses to full texts. Our method first leverages the structural properties of a semantic network in order to model arbitrary linguistic items through a unified probabilistic representation, and then compares the linguistic items in terms of their representations. We report state-of-the-art performance on multiple datasets pertaining to three different levels: senses, words, and texts.

A bayesian framework to explain magnitude estimation in the human mind

Frederike H. Petzschner, Stefan Glasauer, Klaas E. Stephan, A Bayesian perspective on magnitude estimation, Trends in Cognitive Sciences, Volume 19, Issue 5, May 2015, Pages 285-293, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.03.002.

Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia.

Novelty detection as a way for enhancing learning capabilities of a robot, and a brief but interesting survey of motivational theories and their difference with attention

Y. Gatsoulis, T.M. McGinnity, Intrinsically motivated learning systems based on biologically-inspired novelty detection, Robotics and Autonomous Systems, Volume 68, June 2015, Pages 12-20, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.02.006.

Intrinsic motivations play an important role in human learning, particularly in the early stages of childhood development, and ideas from this research field have influenced robotic learning and adaptability. In this paper we investigate one specific type of intrinsic motivation, that of novelty detection and we discuss the reasons that make it a powerful facility for continuous learning. We formulate and present one original type of biologically inspired novelty detection architecture and implement it on a robotic system engaged in a perceptual classification task. The results of real-world robot experiments we conducted show how this original architecture conforms to behavioural observations and demonstrate its effectiveness in terms of focusing the system’s attention in areas that are potential for effective learning.

On the process of the brain for detecting similarities, with a proposal for its structure and its timing

Qingfei Chen, Xiuling Liang, Peng Li, Chun Ye, Fuhong Li, Yi Lei, Hong Li, 2015, The processing of perceptual similarity with different features or spatial relations as revealed by P2/P300 amplitude, International Journal of Psychophysiology, Volume 95, Issue 3, March 2015, Pages 379-387, ISSN 0167-8760, DOI: 10.1016/j.ijpsycho.2015.01.009.

Visual features such as “color” and spatial relations such as “above” or “beside” have complex effects on similarity and difference judgments. We examined the relative impact of features and spatial relations on similarity and difference judgments via ERPs in an S1–S2 paradigm. Subjects were required to compare a remembered geometric shape (S1) with a second one (S2), and made a “high” or “low” judgment of either similarity or difference in separate blocks of trials. We found three main differences that suggest that the processing of features and spatial relations engages distinct neural processes. The first difference is a P2 effect in fronto-central regions which is sensitive to the presence of a feature difference. The second difference is a P300 in centro-parietal regions that is larger for difference judgments than for similarity judgments. Finally, the P300 effect elicited by feature differences was larger relative to spatial relation differences. These results supported the view that similarity judgments involve structural alignment rather than simple feature and relation matches, and furthermore, indicate the similarity judgment could be divided into three phases: feature or relation comparison (P2), structural alignment (P3 at 300–400 ms), and categorization (P3 at 450–550 ms).