scholarly journals Integrating Common Ground and Informativeness in Pragmatic Word Learning

2019 ◽  
Author(s):  
Manuel Bohn ◽  
Michael Henry Tessler ◽  
Michael C. Frank
Keyword(s):  
Common Ground ◽  
Contextual Information ◽  
Model Performance ◽  
Specific Information ◽  
Intended Meaning ◽  
Pragmatic Reasoning ◽  
Extended Experiment ◽  
Series Of Experiments ◽  
Generate Model ◽  
Different Sources

Pragmatic inferences are an integral part of language learn- ing and comprehension. To recover the intended meaning of an utterance, listeners need to balance and integrate different sources of contextual information. In a series of experiments, we studied how listeners integrate general expectations about speakers with expectations specific to their interactional his- tory with a particular speaker. We used a Bayesian pragmatics model to formalize the integration process. In Experiments 1 and 2, we replicated previous findings showing that listeners make inferences based on speaker-general and speaker-specific expectations. We then used the empirical measurements from these experiments to generate model predictions about how the two kinds of expectations should be integrated, which we tested in Experiment 3. Experiment 4 replicated and extended Experiment 3 to a broader set of conditions. In both experiments, listeners based their inferences on both types of expectations. We found that model performance was also consistent with this finding; with better fit for a model which incorporated both general and specific information compared to baselines incorporating only one type. Listeners flexibly integrate different forms of social expectations across a range of contexts, a process which can be described using Bayesian models of pragmatic reasoning.

scholarly journals The pervasive role of pragmatics in early language

2019 ◽  
Author(s):  
Manuel Bohn ◽  
Michael C. Frank
Keyword(s):  
Language Learning ◽  
Common Ground ◽  
Social Cues ◽  
Social Reasoning ◽  
Early Language ◽  
Computational Framework ◽  
Linguistic Pragmatics ◽  
Developmental Continuity ◽  
Pragmatic Reasoning

Language is a fundamentally social endeavor. Pragmatics is the study of how speakers and listeners use social reasoning to go beyond the literal meanings of words to interpret language in context. In this review, we take a pragmatic perspective on language development and argue for developmental continuity between early non-verbal communication, language learning, and linguistic pragmatics. We link phenomena from these different literatures by relating them to a computational framework (the rational speech act framework), which conceptualizes communication as fundamentally inferential and grounded in social cognition. The model specifies how different information sources (linguistic utterances, social cues, common ground) are combined when making pragmatic inferences. We present evidence in favor of this inferential view and review how pragmatic reasoning supports children’s learning, comprehension, and use of language.

The Effects of Context on the Comprehension of Graphic Symbols

1993 ◽  
Vol 37 (8) ◽  
pp. 511-515 ◽  
Author(s):  
Mark Vukelich ◽  
Leslie A. Whitaker
Keyword(s):  
University Students ◽  
Contextual Information ◽  
Context Condition ◽  
Intended Meaning ◽  
Graphic Symbols ◽  
Use Context ◽  
Symbol Comprehension ◽  
Information Comprehension ◽  
Comprehension Accuracy

When graphic symbols are used to convey warning information, these symbols must be evaluated for effectiveness prior to their use. In general, the ability of these symbols to convey their intended meaning has been determined in tests which provide no contextual information surrounding the symbols. In the present study, 75 university students were tested to determine their comprehension of twenty different symbols using various context conditions. Verbal context was provided in two forms: full context and partial context. Full context consisted of a two- sentence description of the setting in which the symbol would be presented. Partial context consisted of a more general, two-word description of the use context. The control condition presented the symbols without contextual information. Comprehension was higher when full context was provided with the symbols than when the symbols were presented in isolation. For some symbols, the full context condition resulted in higher comprehension than the partial context condition and the partial context condition resulted in higher comprehension than the no context condition. Comprehension accuracy was also affected by the subject's familiarity with the symbols. Comprehension was higher for symbols rated high in familiarity than for symbols rated lower in familiarity. On the basis of these findings, a recommendation was made that evaluations should provide some form of contextual information along with the symbols to allow a more realistic test of symbol comprehension.

scholarly journals The Pervasive Role of Pragmatics in Early Language

2019 ◽  
Vol 1 (1) ◽  
pp. 223-249 ◽  
Author(s):  
Manuel Bohn ◽  
Michael C. Frank
Keyword(s):  
Language Learning ◽  
Common Ground ◽  
Social Cues ◽  
Social Reasoning ◽  
Early Language ◽  
Computational Framework ◽  
Linguistic Pragmatics ◽  
Developmental Continuity ◽  
Pragmatic Reasoning

Language is a fundamentally social endeavor. Pragmatics is the study of how speakers and listeners use social reasoning to go beyond the literal meanings of words to interpret language in context. In this article, we take a pragmatic perspective on language development and argue for developmental continuity between early nonverbal communication, language learning, and linguistic pragmatics. We link phenomena from these different literatures by relating them to a computational framework (the rational speech act framework), which conceptualizes communication as fundamentally inferential and grounded in social cognition. The model specifies how different information sources (linguistic utterances, social cues, common ground) are combined when making pragmatic inferences. We present evidence in favor of this inferential view and review how pragmatic reasoning supports children's learning, comprehension, and use of language.

scholarly journals Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
David A. Tovar ◽  
Jacob A. Westerberg ◽  
Michele A. Cox ◽  
Kacie Dougherty ◽  
Thomas A. Carlson ◽  
...  
Keyword(s):  
Neural Activity ◽  
Visual Stimulation ◽  
Brain Activity ◽  
Contextual Information ◽  
Univariate Analysis ◽  
Stimulus Presentation ◽  
Population Level ◽  
Granular Cell ◽  
Stimulus Orientation ◽  
Specific Information

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

scholarly journals Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data

2020 ◽  
Vol 10 (24) ◽  
pp. 8924
Author(s):  
Antreas Pogiatzis ◽  
Georgios Samakovitis
Keyword(s):  
Conditional Random Fields ◽  
Short Term Memory ◽  
Model Performance ◽  
Design Feature ◽  
Sensitive Information ◽  
Context Aware ◽  
Improve Model ◽  
Unseen Data ◽  
Memory Network ◽  
Series Of Experiments

Information privacy is a critical design feature for any exchange system, with privacy-preserving applications requiring, most of the time, the identification and labelling of sensitive information. However, privacy and the concept of “sensitive information” are extremely elusive terms, as they are heavily dependent upon the context they are conveyed in. To accommodate such specificity, we first introduce a taxonomy of four context classes to categorise relationships of terms with their textual surroundings by meaning, interaction, precedence, and preference. We then propose a predictive context-aware model based on a Bidirectional Long Short Term Memory network with Conditional Random Fields (BiLSTM + CRF) to identify and label sensitive information in conversational data (multi-class sensitivity labelling). We train our model on a synthetic annotated dataset of real-world conversational data categorised in 13 sensitivity classes that we derive from the P3P standard. We parameterise and run a series of experiments featuring word and character embeddings and introduce a set of auxiliary features to improve model performance. Our results demonstrate that the BiLSTM + CRF model architecture with BERT embeddings and WordShape features is the most effective (F1 score 96.73%). Evaluation of the model is conducted under both temporal and semantic contexts, achieving a 76.33% F1 score on unseen data and outperforms Google’s Data Loss Prevention (DLP) system on sensitivity labelling tasks.

scholarly journals Computing the meaning of the assertive speech act by a software agent

2017 ◽  
Vol 1 (1) ◽  
pp. 20
Author(s):  
Brian Nolan
Keyword(s):  
Common Ground ◽  
Speech Act ◽  
Contextual Information ◽  
Software Agent ◽  
Cultural Knowledge ◽  
Illocutionary Force ◽  
What Is Said ◽  
Situation Type ◽  
The Common ◽  
The Relationship

This paper examines the nature of the assertive speech act of Irish. We examine the syntactical constructional form of the assertive to identify its constructional signature. We consider the speech act as a construction whose meaning as an utterance depends on the framing situation and context, along with the common ground of the interlocutors. We identify how the assertive speech act is formalised to make it computer tractable for a software agent to compute its meaning, taking into account the contribution of situation, context and a dynamic common ground. Belief, desire and intention play a role in <em>what is meant</em> as against <em>what is said</em>. The nature of knowledge, and how it informs common ground, is explored along with the relationship between knowledge and language. Computing the meaning of a speech act in the situation requires us to consider the level of the interaction of all these dimensions. We argue that the contribution of lexicon and grammar, with the recognition of belief, desire and intentions in the situation type and associated illocutionary force, sociocultural conventions of the interlocutors along with their respective general and cultural knowledge, their common ground and other sources of contextual information are all important for representing meaning in communication. We show that the influence of the situation, context and common ground feeds into the utterance meaning derivation. The ‘<em>what is said’</em> is reflected in the event and its semantics, while the ‘<em>what is meant’</em> is derived at a higher level of abstraction within a situation.

scholarly journals Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables

2021 ◽  
Author(s):  
Theresa Maria Rausch ◽  
Tobias Albrecht ◽  
Daniel Baier
Keyword(s):  
Time Series ◽  
Call Center ◽  
Contextual Information ◽  
Waiting Times ◽  
Dynamic Environment ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Predictor Variables ◽  
Harmonic Regression ◽  
E Mail

AbstractModern call centers require precise forecasts of call and e-mail arrivals to optimize staffing decisions and to ensure high customer satisfaction through short waiting times and the availability of qualified agents. In the dynamic environment of multi-channel customer contact, organizational decision-makers often rely on robust but simplistic forecasting methods. Although forecasting literature indicates that incorporating additional information into time series predictions adds value by improving model performance, extant research in the call center domain barely considers the potential of sophisticated multivariate models. Hence, with an extended dynamic harmonic regression (DHR) approach, this study proposes a new reliable method for call center arrivals’ forecasting that is able to capture the dynamics of a time series and to include contextual information in form of predictor variables. The study evaluates the predictive potential of the approach on the call and e-mail arrival series of a leading German online retailer comprising 174 weeks of data. The analysis involves time series cross-validation with an expanding rolling window over 52 weeks and comprises established time series as well as machine learning models as benchmarks. The multivariate DHR model outperforms the compared models with regard to forecast accuracy for a broad spectrum of lead times. This study further gives contextual insights into the selection and optimal implementation of marketing-relevant predictor variables such as catalog releases, mail as well as postal reminders, or billing cycles.

Acquisition of Pragmatics

2017 ◽  
Author(s):  
Myrto Grigoroglou ◽  
Anna Papafragou
Keyword(s):  
Figurative Language ◽  
Intended Meaning ◽  
Pragmatic Development ◽  
Fine Grained ◽  
Theoretical Approaches ◽  
Pragmatic Communication ◽  
Pragmatic Reasoning ◽  
Pragmatic Aspects ◽  
New Evidence ◽  
Considerable Body

To become competent communicators, children need to learn that what a speaker means often goes beyond the literal meaning of what the speaker says. The acquisition of pragmatics as a field is the study of how children learn to bridge the gap between the semantic meaning of words and structures and the intended meaning of an utterance. Of interest is whether young children are capable of reasoning about others’ intentions and how this ability develops over time. For a long period, estimates of children’s pragmatic sophistication were mostly pessimistic: early work on a number of phenomena showed that very young communicators were egocentric, oblivious to other interlocutors’ intentions, and overall insensitive to subtle pragmatic aspects of interpretation. Recent years have seen major shifts in the study of children’s pragmatic development. Novel methods and more fine-grained theoretical approaches have led to a reconsideration of older findings on how children acquire pragmatics across a number of phenomena and have produced a wealth of new evidence and theories. Three areas that have generated a considerable body of developmental work on pragmatics include reference (the relation between words or phrases and entities in the world), implicature (a type of inferred meaning that arises when a speaker violates conversational rules), and metaphor (a case of figurative language). Findings from these three domains suggest that children actively use pragmatic reasoning to delimit potential referents for newly encountered words, can take into account the perspective of a communicative partner, and are sensitive to some aspects of implicated and metaphorical meaning. Nevertheless, children’s success with pragmatic communication is fragile and task-dependent.

Optimizing the Accuracy of Entity-Based Data Integration of Multiple Data Sources Using Genetic Programming Methods

2012 ◽  
Vol 3 (1) ◽  
pp. 72-82 ◽  
Author(s):  
Yinle Zhou ◽  
Ali Kooshesh ◽  
John Talburt
Keyword(s):  
Genetic Programming ◽  
Data Integration ◽  
Synthetic Data ◽  
Multiple Data ◽  
Null Value ◽  
Selection Operators ◽  
Series Of Experiments ◽  
Null Values ◽  
Selection Operator ◽  
Different Sources

Entity-based data integration (EBDI) is a form of data integration in which information related to the same real-world entity is collected and merged from different sources. It often happens that not all of the sources will agree on one value for a common attribute. These cases are typically resolved by invoking a rule that will select one of the non-null values presented by the sources. One of the most commonly used selection rules is called the naïve selection operator that chooses the non-null value provided by the source with the highest overall accuracy for the attribute in question. However, the naïve selection operator will not always produce the most accurate result. This paper describes a method for automatically generating a selection operator using methods from genetic programming. It also presents the results from a series of experiments using synthetic data that indicate that this method will yield a more accurate selection operator than either the naïve or naïve-voting selection operators.

Dominant Meanings Approach Towards Individualized Web Search for Learning Environments

2008 ◽  
pp. 2376-2393
Author(s):  
M. A. Razek ◽  
C. Frasson
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Individual Performance ◽  
Knowledge Bases ◽  
Specific Information ◽  
Intended Meaning ◽  
Web Based ◽  
Dominant Meaning ◽  
Web Based Learning ◽  
Adaptive Hypermedia Systems

This chapter describes how we can use dominant meaning to improve a Web-based learning environment. For sound adaptive hypermedia systems, we need updated knowledge bases from many kinds of resource (alternative explanations, examples, exercises, images, applets, etc.). The large amount of information available on the Web can play a prominent role in building these knowledge bases. Using the Internet without search engines to find specific information is like wandering aimlessly in the ocean and trying to catch a specific fish. It is obvious, however, that search engines are not intended to adapt to individual performance. Our new technique, based on dominant meaning, is used to individualize a query and search result. By dominant meaning, we refer to a set of keywords that best fits an intended meaning of the target word. Our experiments show that the dominant meanings approach greatly improves retrieval effectiveness.

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