Parsing with parallelism : a spreading-activation model of inference processing during text understanding

The past decade of reseatch in Natural Language Processing has universally recognized that, since natural language input is almost always ambiguous with respect to its pragmatic implications, its syntactic parse, and even its lexical analysis (i.e., choice of correct word-sense for an ambiguous word), processing natural language input requires decisions about word meanings, syntactic structure, and pragmatic inferences. The lexical, syntactic, and pragmatic levels of inferencing are not as disparate as they have often been treated in both psychological and artificial intelligence research. In fact, these three levels of analysis interact to form a joint interpretation of text.ATLAST (A Three-level Language Analysis SysTem) is an implemented integration of human language understanding at the lexical, the syntactic, and the pragmatic levels. For psychological validity, ATLAST is based on results of experiments with human subjects. The ATLAST model uses a new architecture which was developed to incorporate three features: spreading activation memory, two-stage syntax, and parallel processing of syntax and semantics. It is also a new framework within which to interpret and tackle unsolved problems through implementation and experimentation

ON MONITORING LANGUAGE CHANGE WITH THE SUPPORT OF CORPUS PROCESSING

One of the fundamental characteristics of language is that it can change over time. One method to monitor the change is by observing its corpora: a structured language documentation. Recent development in technology, especially in the field of Natural Language Processing allows robust linguistic processing, which support the description of diverse historical changes of the corpora. The interference of human linguist is inevitable as it determines the gold standard, but computer assistance provides considerable support by incorporating computational approach in exploring the corpora, especially historical corpora. This paper proposes a model for corpus development, where corpus are annotated to support further computational operations such as lexicogrammatical pattern matching, automatic retrieval and extraction. The corpus processing operations are performed by local grammar based corpus processing software on a contemporary Indonesian corpus. This paper concludes that data collection and data processing in a corpus are equally crucial importance to monitor language change, and none can be set aside

Virtual personal assistant

Abstract This report discusses ways in which new technology could be harnessed to create an intelligent Virtual Personal Assistant (VPA) with a focus on user-based information. It will look at examples of intelligent programs with natural language processing that are currently available, with different categories of support, and examine the potential usefulness of one specific piece of software as a VPA. This engages the ability to communicate socially through natural language processing, holding (and analysing) information within the context of the user. It is suggested that new technologies may soon make the idea of virtual personal assistants a reality. Experiments conducted on this system, combined with user testing, have provided evidence that a basic program with natural language processing algorithms in the form of a VPA, with basic natural language processing and the ability to function without the need for other type of human input (or programming) may already be viable

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction