Typical event sequences as licensors of direct object ellipsis in Russian

2018 ◽  
Vol 41 (2) ◽  
pp. 179-212
Author(s):  
Marjorie McShane
Keyword(s):  
Intelligent Agents ◽  
Cognitive Modeling ◽  
Direct Object ◽  
Event Sequence ◽  
Language Understanding ◽  
Modeling Effort ◽  
Event Sequences ◽  
Sequence Of Events ◽  
Typical Sequence ◽  
Typical Event

Abstract This paper extends the computationally-oriented theory of ellipsis presented in McShane’s A Theory of Ellipsis (2005) by introducing the feature typical event sequence. It is argued that, in Russian, the presence of a typical sequence of events in a pair of clauses can be the key feature licensing the ellipsis of the latter’s direct object. The linguistic analysis contributes to a larger cognitive modeling effort aimed at configuring language-endowed intelligent agents with human-level language understanding capabilities.

scholarly journals A Variational Point Process Model for Social Event Sequences

2020 ◽  
Vol 34 (01) ◽  
pp. 173-180
Author(s):  
Zhen Pan ◽  
Zhenya Huang ◽  
Defu Lian ◽  
Enhong Chen
Keyword(s):  
Point Process ◽  
Real World ◽  
Process Model ◽  
Classification Model ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Modeling ◽  
Stochastic Point Process ◽  
Feature Based ◽  
Real World Datasets

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

Arriving from Delphi at O'Hare: Predictive Cognitive Engineering in the O'Hare Modernization Project and Beyond

2005 ◽  
Vol 49 (1) ◽  
pp. 108-112
Author(s):  
Steven Estes
Keyword(s):  
Cognitive Modeling ◽  
Mental Workload ◽  
Cognitive Models ◽  
Cognitive Engineering ◽  
Work Time ◽  
Modeling Effort ◽  
International Airport

This paper describes a cognitive modeling effort for the O'Hare Modernization Project (OMP). Beginning with a statement of the problem, it describes how cognitive modeling was used to measure the mental workload and work time of controllers running various positions at O'Hare International Airport, both under the current airport configurations and a future set of configurations (proposed in the OMP). The O'Hare case is used as an exemplar of the type of data that can be acquired with relatively simple cognitive models

Context for language understanding by intelligent agents

2019 ◽  
Vol 14 (4) ◽  
pp. 415-449 ◽  
Author(s):  
Marjorie McShane ◽  
Sergei Nirenburg
Keyword(s):  
Intelligent Agents ◽  
Language Understanding

A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support

2000 ◽  
Vol 4 (6) ◽  
pp. 403-407 ◽  
Author(s):  
M. Shamim Khan ◽  
◽  
Alex Chong ◽  
Tom Gedeon
Keyword(s):  
Decision Support ◽  
Dynamic System ◽  
Hebbian Learning ◽  
Real Life ◽  
Cognitive Maps ◽  
Causal Link ◽  
Fuzzy Cognitive Maps ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Encoding

Differential Hebbian Learning (DHL) was proposed by Kosko as an unsupervised learning scheme for Fuzzy Cognitive Maps (FCMs). DHL can be used with a sequence of state vectors to adapt the causal link strengths of an FCM. However, it does not guarantee learning of the sequence by the FCM and no concrete procedures for the use of DHL has been developed. In this paper a formal methodology is proposed for using DHL in the development of FCMs in a decision support context. The four steps in the methodology are: (1) Creation of a crisp cognitive map; (2) Identification of event sequences for use in DHL; (3) Event sequence encoding using DHL; (4) Revision of the trained FCM. Feasibility of the proposed methodology is demonstrated with an example involving a dynamic system with feedback based on a real-life scenario.

Comprehension of before and after in logical and arbitrary sequences

1977 ◽  
Vol 4 (2) ◽  
pp. 247-256 ◽  
Author(s):  
Lucia A. French ◽  
Ann L. Brown
Keyword(s):  
School Children ◽  
Event Sequences ◽  
Sequence Of Events ◽  
Before And After ◽  
Contextual Support

ABSTRACTPre-school children were required to act out a series of two-event sequences conjoined by either before or after. The sentences to be acted out consisted of either a meaningfully or an arbitrarily ordered sequence of events. Performance was markedly superior for meaningfully ordered sequences. It is suggested that the meanings of before and after must be acquired in situations which provide contextual support, and only then can be applied in situations which do not provide such support.

Construction and Reconstruction of Social Event Sequences: A Developmental and Comparative Study

1985 ◽  
Vol 56 (2) ◽  
pp. 511-518 ◽  
Author(s):  
Louis Oppenheimer ◽  
Monique Groot
Keyword(s):  
Comparative Study ◽  
Cognitive Ability ◽  
Event Sequence ◽  
Guttman Scale ◽  
Social Event ◽  
Event Sequences ◽  
Initial Event ◽  
Social Events

78 children from three age levels ( M ages 5.7, 7.5, and 9.0 yr.) and from Dutch and nonDutch parents were presented three tasks to index their abilities to construct and reconstruct sequences of social events. The results show that all tasks form a stochastic Guttman-scale indicating that all measures tap the same cognitive ability. The resulting hierarchy of the tasks suggests that the ability to complete an event sequence by selecting from two additional events, i.e., the initial and the final events, preceded ability to select from two alternative events either the correct final or the correct initial event. The latter two constructional abilities preceded ability to reconstruct sequences of social events involving memorizing the sequence and anticipatory and reversibility competences.

scholarly journals Konflik tussen groepe: Die verhouding Verkeersbeampte en Motoris

1983 ◽  
Author(s):  
A. B. Bosshof ◽  
M. De V Visser
Keyword(s):  
Negative Relationship ◽  
Operational Definition ◽  
Negative Effects ◽  
Sequence Of Events ◽  
Conflict Situations ◽  
Typical Sequence ◽  
Residual Elements ◽  
The One ◽  
Definition Of

An operational definition of the concept of conflict is given and the implications of the definition discussed. The course of a conflict and the processes involved are described. An effort is made to show that the typical sequence of events in a conflict can be used to explain the (postulated) bad relationship between the motorist on the one hand and the traffic officer on the other hand. The negative effects of residual elements of unsatisfactorily resolved conflict situations between the two groups are emphasized. It is suggested that industrial psychologists have a role to play in the long-term solution of the negative relationship. Opsomming 'n Operasionele definisie van die begrip konflik word gegee en die implikasies daarvan bespreek. Die verloop van 'n konflik en die prosesse betrokke, word beskrywe. 'n Poging word aangewend om aan te toon dat die tipiese verloop van 'n konflik gebruik kan word om die (gepostuleerde) swak verhouding tussen motoriste aan die een kant en die verkeersbeamptes aan die ander kant te verklaar. Die negatiewe uitwerking van residuele effekte van onbevredigend opgeloste konfliksituasies tussen die twee groepe persone word beklemtoon. Daar word aan die hand gedoen dat bedryfsielkundiges 'n rol speel in die langtermynoplossing van die negatiewe verhouding.

Sphingomyelinase-Induced Remodeling of the Erythrocyte Membrane

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1032-1032
Author(s):  
Sip Dinkla ◽  
Katharina Wessels ◽  
Wouter P.R. Verdurmen ◽  
Irma Joosten ◽  
Roland E. Brock ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Chronic Inflammation ◽  
Conflicts Of Interest ◽  
Shape Change ◽  
Red Cells ◽  
Red Cell ◽  
Red Cell Membrane ◽  
Cell Homeostasis ◽  
Sequence Of Events ◽  
Typical Sequence

Abstract Abstract 1032 Sphingomyelinases (SMases) catalyze the hydrolysis of sphingomyelin, a major lipid component of cell membranes, generating phosphocholine and ceramide. Inflammation arising from various diseases including sepsis is known to enhance SMase secretion in the circulation. Also, chronic inflammation typically induces anemia through reduced production and enhanced clearance of red cells, which is often associated with poor disease outcome. Incubation of red cells with plasma of septic patients with enhanced SMase activity has been described to result in ceramide formation and exposure of phosphatidylserine (PS). Therefore, we studied the effect of SMase on various pathophysiologically relevant parameters of red cell homeostasis in fresh and stored erythrocytes. Using time-lapse confocal microscopy we observed a typical sequence of events during SMase treatment of erythrocytes. The cells first lost their discoid shape, PS became externalized, and ultimately there was a sudden loss of cytoplasmic content. Additional confocal microscopy, flow cytometry and immunoblot analyses showed that SMase treatment resulted in ceramide-induced alterations in red cell membrane-cytoskeleton interaction and organization, including lipid raft formation. These changes probably underlie the increase in osmotic fragility, membrane vesiculation and invagination, as well as large hemoglobin aggregates that we also observed. The membrane lipid scrambling, altered morphology, enhanced fragility and presence of rigid membrane patches may all contribute to enhanced removal of sphingomyelinase-exposed red cells. Generation of very low ceramide levels in the membrane by SMase, as detected by Positive-Ion MALDI-TOF MS, already affected red cell shape, membrane rearrangement and osmotic responsiveness. Combined with the observation that 24 hour incubation of red cells with pathological SMase levels induced shape change and PS exposure, these data suggest that red cell homeostasis could well be disturbed by SMase during chronic inflammation. Of note, red cell storage greatly increased the sensitivity to SMase-induced changes. This may well have implications for red cell transfusion in critically ill patients. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

2019 ◽  
Vol 33 ◽  
pp. 4959-4966 ◽  
Author(s):  
Aditya Siddhant ◽  
Anuj Goyal ◽  
Angeliki Metallinou
Keyword(s):  
Intelligent Agents ◽  
Language Model ◽  
Model Performance ◽  
User Interaction ◽  
Spoken Language ◽  
Training Method ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Improve Model ◽  
Improve Model Performance

User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks. We use Embeddings from Language Model (ELMo) to take advantage of unlabeled data by learning contextualized word representations. Additionally, we propose ELMo-Light (ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer. Additionally, we show that the gains from unsupervised transfer techniques can be further improved by supervised transfer. The improvements are more pronounced in low resource settings and when using only 1000 labeled in-domain samples, our techniques match the performance of training from scratch on 10-15x more labeled in-domain data.

scholarly journals Natural Language Understanding (NLU, not NLP) in Cognitive Systems

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 43-56 ◽  
Author(s):  
Marjorie McShane
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Cognitive Modeling ◽  
Cognitive Biases ◽  
Cooperative Behavior ◽  
Holistic Approach ◽  
Natural Language Understanding ◽  
Human Cognition ◽  
Language Understanding ◽  
Cognitive Agents

Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent’s understanding of its own plans and goals; its dynamic modeling of its interlocutor’s knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community. Instead, it requires a holistic approach to cognitive modeling of the type we are pursuing in a paradigm called OntoAgent.

Sign in / Sign up

Export Citation Format

Share Document