Acquisition Of Semantic Knowledge Research Articles

Imagine & immerse yourself: Does visuospatial imagery moderate learning in virtual reality?

Findings on the effectiveness of learning in immersive virtual reality (IVR) are mixed. While studies suggest that the sense of spatial presence in IVR enhances enjoyment and thus learning, it is unclear whether IVR also presents information in a way that is more conducive to learning than less immersive presentations. Because IVR presents spatial details more accurately than less immersive media, in addition to motivational effects, IVR presentations should promote the acquisition of spatial and (aurally presented) semantic knowledge. Research also suggests that the effects of IVR presentation on spatial presence, enjoyment, and learning may be offset by visuospatial imagery skills. We tested these assumptions by conducting a randomized laboratory experiment with two conditions in which a virtual learning environment was presented using either IVR technology or less immersive desktop VR. Results show that IVR did not provide superior support the acquisition of spatial and semantic knowledge, and this was not moderated by visuospatial imagery. However, spatial presence mediated the acquisition of semantic knowledge through enjoyment in both conditions, and this effect was moderated by learners' level of imagery: IVR's advantages over desktop VR in promoting spatial presence, enjoyment, and semantic knowledge diminished as learners' visuospatial imagery increased.

Investigation of the Relationship between Semantic Information and Verbal Working Memory in Children with Typical Developing, Down Syndrome and Autism Spectrum Disorder

The aim of this study is to investigate the relationship between semantic knowledge and verbal working memory (VWM) in children with typical development (TD), Down syndrome (DS) and autism spectrum disorder (ASD).Descriptive research and relational research methods were used in the design of the research.Children in the study group were matched with the Raven Colored Progressive Matrices Test according to their non-verbal intelligence score.The study group of the study consisted of 17 TD children aged 4-5 years and 14 DS and 15 ASD children whose nonverbal intelligence ages are between 4-5 years.Semantic knowledge was evaluated in the dimensions of vocabulary breath and vocabulary depth.The language sample collected in narrative context was used to evaluate of vocabulary breath. Vocabulary Depth Test (VDT) was used to assess vocabulary depth.VWM was assessed with the Nonsense Word Repetition Test (NWR).The results of the research revealed that all groups showed significant differences in vocabulary breadth, vocabulary depth and VWM performances.It was found that there was a significant and positive relationship between vocabulary breadth, vocabulary depth and VWM in all groups.VMW predicted vocabulary breadth and vocabulary depth in children with TD and DS.Vocabulary breadth was found to predict vocabulary depth in children with TD and ASD.However, VMW did not predict vocabulary breadth and depth in ASD.The results obtained from the study show that the results differ in the two different diagnostic groups.While the results were discussed regarding the contribution of verbal working memory in the acquisition of semantic knowledge in DS, the importance of word width in the acquisition of word depth in ASD was discussed. In addition, it has been suggested to examine different variables that may be effective in the development of semantic knowledge in both diagnostic groups.

Contingency of Semantic Generalization on Episodic Specificity: Variations Across Development

Popular computational models of memory have posited that the formation of new semantic knowledge relies on generalization from memories of specific but related episodes, at least when it occurs rapidly. This view predicts a contingency between new generalizations and episodic memory. However, very young children readily accumulate semantic knowledge at a time when their episodic memory capacities are fragile. This phenomenon challenges the notion that semantic knowledge acquisition and rapid generalization are necessarily gated by episodic memory. Here, we tested whether generalization depends on memory for individual episodes in children from 3 to 8 years of age and contrasted their performance with adults. We found that the interdependence of generalization and episodic memory changed across development. Young adults’ generalization success was contingent on their memories for an item linked to its episodic context. In contrast, generalization by young children was contingent on memories of the specific identity of items and the availability of the conceptual common ground linking related episodes. This age-related contrast favors models of memory that can account for the relations between rapid generalization and episodic memory in immature systems.

The creation and evaluation of a grammar pattern list for the most frequent academic verbs

The current research project was designed according to the conceptualization that vocabulary knowledge is multidimensional (Jaen, 2007; Qian & Schedl, 2004) and the learning of grammatical and collocational patterns should be considered as important as, if not more important than, the acquisition of semantic knowledge of single words. The purposes of the project were to (1) propose a method of extracting grammar patterns for the most frequent content words (verbs, nouns, adjectives and adverbs) in a given corpus; and (2) derive a tentative grammar pattern list for the 115 most frequent academic verbs.The extraction of grammar patterns was realized through a rule-based programming approach, where the researchers coded all possible patterns of the most frequent academic verbs and used the script to extract grammar patterns from a given corpus. The extracted grammar patterns were compiled with corpus-based frequency information and developed into a grammar pattern list for the top-frequency academic verbs. The grammar pattern list is expected to complement the widely used EAP (English for academic purposes) word list, the Academic Vocabulary List (Gardner & Davies, 2014), by specifying the frequency of each verb patterns (e.g. provide n with n, select n from n, base n on n).

PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech.

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system.

Automated Semantic Knowledge Acquisition From Sensor Data

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended $k$ - means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.

Provenance and Validation from the Humanities to Automatic Acquisition of Semantic Knowledge and Machine Reading for News and Historical Sources Indexing/Summary

This paper, as a conlcusion to this special issue, presents the future work that is being carried out at NTU Singapore in collaboration with Microsoft Research and Microsoft Azure for Research. For our research team the real frontier research in world histories starts when we want to use computers to structure historical information, model historical narratives, simulate theoretical large scale hypotheses, and incent world historians to use virtual assistants and/or engage them in teamwork using social media and/or seduce them with immersive spaces to provide new learning and sharing environments, in which new things can emerge and happen: “You do not know which will be the next idea. Just repeating the same things is not enough” (Carlo Rubbia, 1984 Nobel Price in Physics, at Nanyang Technological University on January 19, 2016).

Born in the USA: a comparison of modals and nominal quantifiers in child language

One of the challenges confronted by language learners is to master the interpretation of sentences with multiple logical operators (e.g., nominal quantifiers, modals, negation), where different interpretations depend on different scope assignments. Five-year-old children have been found to access some readings of potentially ambiguous sentences much less than adults do (Lidz and Musolino, Lang Acquis 13(2):73–102, 2006; Musolino, Universal Grammar and the acquisition of semantic knowledge, 1998; Musolino and Lidz, Lang Acquis 11(4):277–291, 2003, among many others). Recently, Gualmini et al. (Nat Lang Semant 16:205–237, 2008) have shown that, by careful contextual manipulation, it is possible to evoke some of the putatively unavailable interpretations from young children. Their proposal is quite general, but the focus of their work was on sentences involving nominal quantifiers and negation. The present paper extends this investigation to sentences with modal expressions. The results of our two experimental studies reveal that, in potentially ambiguous sentences with modal expressions, the kinds of contextual manipulations introduced by Gualmini and colleagues do not suffice to explain children’s initial scope interpretations. In response to the recalcitrant data, we propose a new three-stage model of the acquisition of scope relations. Most importantly, at the initial stage, child grammars make available only one interpretation of negative sentences with modal expressions. We call this the Unique Scope Assignment (USA) stage.

Design and evaluation of a cognitive approach for disseminating semantic knowledge and content in opportunistic networks

In cyber-physical convergence scenarios information flows seamlessly between the physical and the cyber worlds. Here, users’ mobile devices represent a natural bridge through which users process acquired information and perform actions. The sheer amount of data available in this context calls for novel, autonomous and lightweight data-filtering solutions, where only relevant information is finally presented to users. Moreover, in many real-world scenarios data is not categorised in predefined topics, but it is generally accompanied by semantic descriptions possibly describing users’ interests. In these complex conditions, user devices should autonomously become aware not only of the existence of data in the network, but also of their semantic descriptions and correlations between them. To tackle these issues, we present a set of algorithms for knowledge and data dissemination in opportunistic networks, based on simple and very effective models (called cognitive heuristics) coming from cognitive sciences. We show how to exploit them to disseminate both semantic data and the corresponding data items. We provide a thorough performance analysis, under various different conditions comparing our results against non-cognitive solutions. Simulation results demonstrate the superior performance of our solution towards a more effective semantic knowledge acquisition and representation, and a more tailored content acquisition.

Generalization from episodic memories across time: A route for semantic knowledge acquisition

The storage of input regularities, at all levels of processing complexity, is a fundamental property of the nervous system. At high levels of complexity, this may involve the extraction of associative regularities between higher order entities such as objects, concepts and environments across events that are separated in space and time. We propose that such a mechanism provides an important route towards the formation of higher order semantic knowledge. The present study assessed whether subjects were able to extract complex regularities from multiple associative memories and whether they could generalize this regularity knowledge to new items. We used a memory task in which subjects were required to learn face-location associations, but in which certain facial features were predictive of locations. We assessed generalization, as well as memory for arbitrary stimulus components, over a 4-h post-encoding consolidation period containing wakefulness or sleep. We also assessed the stability of regularity knowledge across a period of several weeks thereafter. We found that subjects were able to detect the regularity structure and use it in a generalization task. Interestingly, the performance on this task increased across the 4hr post-learning period. However, no differential effects of cerebral sleep and wake states during this interval were observed. Furthermore, it was found that regularity extraction hampered the storage of arbitrary facial features, resulting in an impoverished memory trace. Finally, across a period of several weeks, memory for the regularity structure appeared very robust whereas memory for arbitrary associations showed steep forgetting. The current findings improve our understanding of how regularities across memories impact memory (trans)formation.

The Body of Evidence: What Can Neuroscience Tell Us about Embodied Semantics?

Semantic knowledge is based on the way we perceive and interact with the world. However, the jury is still out on the question: to what degree are neuronal systems that subserve acquisition of semantic knowledge, such as sensory-motor networks, involved in its representation and processing? We will begin with a critical evaluation of the main behavioral and neuroimaging methods with respect to their capability to define the functional roles of specific brain areas. Any behavioral or neuroscientific measure is a conflation of representations and processes. Hence, a combination of behavioral and neurophysiological interactions as well as time-course information is required to define the functional roles of brain areas. This will guide our review of the empirical literature. Most research in this area has been done on semantics of concrete words, where clear theoretical frameworks for an involvement of sensory-motor systems in semantics exist. Most of this evidence still stems from correlational studies that are ambiguous with respect to the behavioral relevance of effects. Evidence for causal effects of sensory-motor systems on semantic processes is still scarce but evolving. Relatively few neuroscientific studies so far have investigated the embodiment of abstract semantics for words, numbers, and arithmetic facts. Here, some correlational evidence exists, but data on causality are mostly absent. We conclude that neuroimaging data, just as behavioral data, have so far not disentangled the fundamental link between process and representation. Future studies should therefore put more emphasis on the effects of task and context on semantic processing. Strong conclusions can only be drawn from a combination of methods that provide time-course information, determine the connectivity among poly- or amodal and sensory-motor areas, link behavioral with neuroimaging measures, and allow causal inferences. We will conclude with suggestions on how this could be accomplished in future research.

Developmental dissociations between lexical reading and comprehension: Evidence from two cases of hyperlexia

We report two cases of developmental hyperlexia – JY and AD – who performed at normal levels or above in converting print into speech, but who were very impaired in spoken and written word comprehension. Our investigations focussed on whether these cases displayed evidence for normal acquisition of lexical reading skills, as indexed by unimpaired performance for age in reading aloud a set of irregular words, despite poor acquisition of semantic knowledge of the same words. In both cases, this dissociation was evident. The pattern of results was also demonstrated at an item level: the two cases showed no significant differences in reading accuracy for irregular words which they could define than for those which they could not. The results provide further evidence for the existence of a direct-lexical route from orthography to phonology, which is not necessarily mediated by semantic knowledge.

Constraint acquisition for Entity-Relationship models

We establish search algorithms from the area of propositional logic as invaluable tools for the semantic knowledge acquisition in the conceptual database design phase. The acquisition of such domain knowledge is crucial for the quality of the target database. Integrity constraints are conditions that capture the semantics of the application domain under consideration. They restrict the databases to those that are considered meaningful to the application at hand. In practice, the decision of specifying a constraint is very important and extremely challenging. We show how techniques from propositional logic can be utilised to offer decision support for specifying Boolean and multivalued dependencies between properties of entities and relationships in conceptual databases. In particular, we use a search version of SAT-solvers to semi-automatically generate sample databases for this class of dependencies in Entity-Relationship models. The sample databases enable design participants to judge, justify, convey and test their understanding of the semantics of the future database. Indeed, the decision by the participants to specify a dependency explicitly is reduced to their decision whether there is some sample database that they can accept as a future database instance.

Charting the acquisition of semantic knowledge in a case of developmental amnesia

We report the acquisition and recall of novel facts by Jon, a young adult with early onset developmental amnesia whose episodic memory is gravely impaired due to selective bilateral hippocampal damage. Jon succeeded in learning some novel facts but compared with a control group his intertrial retention was impaired during acquisition and, except for the most frequently repeated facts, he was also less accurate in correctly sourcing these facts to the experiment. The results further support the hypothesis that despite a severely compromised episodic memory and hippocampal system, there is nevertheless the capacity to accrue semantic knowledge available to recall.

Improving Semi-supervised Acquisition of Semantic Knowledge from Query Logs

Improving Semi-supervised Acquisition of Semantic Knowledge from Query Logs

On the Semantics of the Subset Principle

Virtually all generative accounts of language development assume that syntactic acquisition is guided by a learnability constraint called the Subset Principle (SP). In essence, SP forces learners to initially select the value of a parameter that generates the smallest possible language. Recent work on the acquisition of semantics suggests that there also are semantic subset problems; therefore, one needs a Semantic Subset Principle (SSP) to solve these problems. This article shows that (a) to the extent that semantic subset problems exist, the SSP is not the correct solution to them and (b) that semantic subset problems most likely do not exist in the first place. These conclusions have important implications for theories of the acquisition of semantic knowledge and for the study of language acquisition more generally. First, they provide a basis for delimiting the class of problems that theories of the acquisition of semantics ought to be responsible for. Second, these conclusions underscore the fact that data from empirical investigations of child language can be brought to bear on the formulation of linguistic theory.

A Case of Amnesia at an Early Age

A dissociation between short- and long-term memory (LTM) and between the episodic and the semantic component of LTM is reported in a young girl who became amnesic at the age of 6 after an episode of acute encephalopathy resulting in bilateral frontal, insular, thalamic, ponto-mesencephalic, hippocampal and temporal lesions, as documented by MRI. The girl became amnesic a few months after starting school. A follow-up investigation showed that she was able to learn to read, write and acquire number facts and procedures and to improve her semantic knowledge. Our results show that the features of adult amnesia can also be found in children and that new semantic knowledge can be acquired in spite of an anterograde memory deficit. This dissociation does not agree with theories viewing long- term declarative memory as a unitary process mediated by the hippocampal system, but supports recent hypotheses that the acquisition of semantic knowledge is independent from episodic memory processes, and takes place through spared cortical regions subjacent to the hippocampi (Vargha-Khadem et al., 1997).

The Role of the Central Cholinergic Projections in Cognition: Implications of the Effects of Scopolamine on Discrimination Learning by Monkeys

In humans, administration of the cholinergic antagonist scopolamine impairs the encoding of information into long-term memory and has effects on other cognitive processes. It has been supposed that it is inhibition of the rising cholinergic projections from the basal forebrain, specifically from the basal nucleus of Meynert (NBM) to the neocortex and from the medial septum/vertical limb of the diagonal band of Broca (MS/VDB) to the hippocampus, that results in these cognitive impairments. In this paper, we describe the effects of scopolamine treatment in monkeys on learning different sorts of visual discrimination and visuospatial conditional tasks and compare these results to the effects of lesions of the rising cholinergic projections. Experiments in rodents in which these projections have been selectively destroyed have failed to produce a consensus view of the functions of these two areas. In particular, highly specific immunotoxic lesions of the NBM have largely failed to produce changes in task performance that can be interpreted as resulting from a cognitive impairment. In monkeys, lesions of the NBM produce modest or short-lasting, impairments in visual discrimination learning, retention, and reversal, whereas lesions of the MS/VDB produce large and permanent impairments of certain types of conditional learning. Similar impairments produced by scopolamine in monkeys and additive effects of lesions of the NBM or MS/VDB with scopolamine suggest that scopolamine has these effects by acting on the rising cholinergic pathways rather than on other cholinergic systems in the brain. It is argued that the rising cholinergic projections sustain the functions of the target areas; in the case of the hippocampus in humans, the function is usually regarded as being the analysis of information in a way that is pertinent to the formation of episodic memories and in the case of the neocortex, is the analysis of information in a manner that is relevant to the cognitive processing of on-going events and the acquisition of semantic knowledge.

Constructing an intelligent dictionary help system

This paper discusses different issues in the construction and knowledge representation of an intelligent dictionary help system. The Intelligent Dictionary Help System (IDHS) is conceived as a monolingual (explanatory) dictionary system for human use (Artola and Evrard, 1992). The fact that it is intended for people instead of automatic processing distinguishes it from other systems dealing with the acquisition of semantic knowledge from conventional dictionaries. The system provides various access possibilities to the data, allowing to deduce implicit knowledge from the explicit dictionary information. IDHS deals with reasoning mechanisms analogous to those used by humans when they consult a dictionary. User level functionality of the system has been specified and a prototype has been implemented (Agirre et al., 1994a). A methodology for the extraction of semantic knowledge from a conventional dictionary is described. The method followed in the construction of the phrasal pattern hierarchies required by the parser (Alshawi, 1989) is based on an empirical study carried out on the structure of definition sentences. The results of its application to a real dictionary has shown that the parsing method is particularly suited to the analysis of short definition sentences, as it was the case of the source dictionary. As a result of this process, the characterization of the different lexical-semantic relations between senses is established by means of semantic rules (attached to the patterns); these rules are used for the initial construction of the Dictionary Knowledge Base (DKB). The representation schema proposed for the DKB (Agirre et al., 1994b) is basically a semantic network of frames representing word senses. After construction of the initial DKB, several enrichment processes are performed on the DKB to add new facts to it; these processes are based on the exploitation of the properties of lexical-semantic relations, and also on specially conceived deduction mechanisms. The result of the enrichment processes show the suitability of the representation schema chosen to deduce implicit knowledge. Erroneous deductions are mainly due to incorrect word sense disambiguation.