scholarly journals On Resolving Ambiguous Anaphoric Expressions in Imperative Discourse

2019 ◽  
Vol 33 ◽  
pp. 6957-6964
Author(s):  
Vasanth Sarathy ◽  
Matthias Scheutz
Keyword(s):  
General Class ◽  
State Of The Art ◽  
Contextual Information ◽  
Anaphora Resolution ◽  
Proof Of Concept ◽  
Language Understanding ◽  
Concept System ◽  
Knowledge Based ◽  
Current State ◽  
Reasoning Systems

Anaphora resolution is a central problem in natural language understanding. We study a subclass of this problem involving object pronouns when they are used in simple imperative sentences (e.g., “pick it up.”). Specifically, we address cases where situational and contextual information is required to interpret these pronouns. Current state-of-the art statisticallydriven coreference systems and knowledge-based reasoning systems are insufficient to address these cases. In this paper, we introduce, with examples, a general class of situated anaphora resolution problems, propose a proof-of-concept system for disambiguating situated pronouns, and discuss some general types of reasoning that might be needed.

Word Sense Disambiguation

2013 ◽  
pp. 22-51
Author(s):  
Pushpak Bhattacharyya ◽  
Mitesh Khapra
Keyword(s):  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Current Trend ◽  
General Purpose ◽  
Word Sense ◽  
Domain Specific ◽  
Knowledge Based ◽  
Current State ◽  
Sense Disambiguation ◽  
State Of Affairs

This chapter discusses the basic concepts of Word Sense Disambiguation (WSD) and the approaches to solving this problem. Both general purpose WSD and domain specific WSD are presented. The first part of the discussion focuses on existing approaches for WSD, including knowledge-based, supervised, semi-supervised, unsupervised, hybrid, and bilingual approaches. The accuracy value for general purpose WSD as the current state of affairs seems to be pegged at around 65%. This has motivated investigations into domain specific WSD, which is the current trend in the field. In the latter part of the chapter, we present a greedy neural network inspired algorithm for domain specific WSD and compare its performance with other state-of-the-art algorithms for WSD. Our experiments suggest that for domain-specific WSD, simply selecting the most frequent sense of a word does as well as any state-of-the-art algorithm.

UNDERSTANDING SOFTWARE PRODUCTIVITY: TOWARDS A KNOWLEDGE-BASED APPROACH

1991 ◽  
Vol 01 (03) ◽  
pp. 293-321 ◽  
Author(s):  
WALT SCACCHI
Keyword(s):  
Modeling And Simulation ◽  
State Of The Art ◽  
Simulation System ◽  
Knowledge Based ◽  
Current State ◽  
Software Productivity ◽  
Productivity Modeling

What affects software productivity and how do we improve it? This report examines the current state of the art in understanding and measuring software productivity. In turn, it describes a framework for understanding software productivity, identifies some fundamentals of measurement, surveys selected studies of software productivity, and identifies variables that affect software productivity. Then, a radical alternative to current approaches is suggested: to construct, evaluate, deploy, and evolve a knowledge-based "software productivity modeling and simulation system."

scholarly journals Electrode-free visual prosthesis/exoskeleton control using augmented reality glasses in a first proof-of-technical-concept study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Simon Hazubski ◽  
Harald Hoppe ◽  
Andreas Otte
Keyword(s):  
Augmented Reality ◽  
State Of The Art ◽  
Visual Prosthesis ◽  
Control Mechanisms ◽  
Proof Of Concept ◽  
End Effector ◽  
Concept Study ◽  
Current State ◽  
First Time ◽  
Robust Systems

Abstract In the field of neuroprosthetics, the current state-of-the-art method involves controlling the prosthesis with electromyography (EMG) or electrooculography/electroencephalography (EOG/EEG). However, these systems are both expensive and time consuming to calibrate, susceptible to interference, and require a lengthy learning phase by the patient. Therefore, it is an open challenge to design more robust systems that are suitable for everyday use and meet the needs of patients. In this paper, we present a new concept of complete visual control for a prosthesis, an exoskeleton or another end effector using augmented reality (AR) glasses presented for the first time in a proof-of-concept study. By using AR glasses equipped with a monocular camera, a marker attached to the prosthesis is tracked. Minimal relative movements of the head with respect to the prosthesis are registered by tracking and used for control. Two possible control mechanisms including visual feedback are presented and implemented for both a motorized hand orthosis and a motorized hand prosthesis. Since the grasping process is mainly controlled by vision, the proposed approach appears to be natural and intuitive.

scholarly journals Using Deep Time Delay Neural Network for Slot Filling in Spoken Language Understanding

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 993
Author(s):  
Zhen Zhang ◽  
Hao Huang ◽  
Kai Wang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Target Word ◽  
State Of The Art ◽  
Contextual Information ◽  
Spoken Language ◽  
Continuous Speech Recognition ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Slot Filling

Modeling the context of a target word is of fundamental importance in predicting the semantic label for slot filling task in Spoken Language Understanding (SLU). Although Recurrent Neural Network (RNN) has shown to successfully achieve the state-of-the-art results for SLU, and Bidirectional RNN is capable of obtaining further improvement by modeling information not only from the past, but also from the future, they only consider limited contextual information of the target word. In order to make the network deeper and hence obtain longer contextual information, we propose to use a multi-layer Time Delay Neural Network (TDNN), which is prevalent in current large vocabulary continuous speech recognition tasks. In particular, we use a TDNN with symmetric time delay offset. To make the stacked TDNN easily trained, residual structures and skip concatenation are adopted. In addition, we further improve the model by introducing ResTDNN-BiLSTM, which combines the advantages of both the residual TDNN and BiLSTM. Experiments on slot filling tasks on the Air Travel Information System (ATIS) and Snips benchmark datasets show the proposed SC-TDNN-C achieves state-of-the-art results without any additional knowledge and data resources. Finally, we review and compare slot filling results by using a variety of existing models and methods.

Knowledge-based expert systems — The State of the Art in the US

1984 ◽  
Vol 1 (2) ◽  
pp. 18-27 ◽  
Author(s):  
F. Hayes-Roth
Keyword(s):  
Expert Systems ◽  
State Of The Art ◽  
Historical Context ◽  
The State ◽  
Knowledge Systems ◽  
Future Trends ◽  
Knowledge Based ◽  
Current State ◽  
The Us

SummaryThis paper aims to describe the current state of knowledge systems technology and its commercialisation in the US. First, knowledge systems are defined and placed in a historical context. The introduction is concluded with a preview of major ideas. The paper will assess the technological state of the art and will survey the current state of commercialisation. Finally, some anticipated future trends will be discussed.

scholarly journals Global-local attention for emotion recognition

2021 ◽  
Author(s):  
Nhat Le ◽  
Khanh Nguyen ◽  
Anh Nguyen ◽  
Bac Le
Keyword(s):  
Emotion Recognition ◽  
State Of The Art ◽  
Contextual Information ◽  
Research Area ◽  
The Past ◽  
Current State ◽  
Substantial Progress ◽  
Human Emotions ◽  
Active Research ◽  
Active Research Area

AbstractHuman emotion recognition is an active research area in artificial intelligence and has made substantial progress over the past few years. Many recent works mainly focus on facial regions to infer human affection, while the surrounding context information is not effectively utilized. In this paper, we proposed a new deep network to effectively recognize human emotions using a novel global-local attention mechanism. Our network is designed to extract features from both facial and context regions independently, then learn them together using the attention module. In this way, both the facial and contextual information is used to infer human emotions, therefore enhancing the discrimination of the classifier. The intensive experiments show that our method surpasses the current state-of-the-art methods on recent emotion datasets by a fair margin. Qualitatively, our global-local attention module can extract more meaningful attention maps than previous methods. The source code and trained model of our network are available at https://github.com/minhnhatvt/glamor-net.

scholarly journals Context Model Acquisition from Spoken Utterances

2017 ◽  
Vol 27 (09n10) ◽  
pp. 1439-1453 ◽  
Author(s):  
Sebastian Weigelt ◽  
Tobias Hey ◽  
Walter F. Tichy
Keyword(s):  
User Study ◽  
Contextual Information ◽  
State Transitions ◽  
Anaphora Resolution ◽  
Context Model ◽  
Language Understanding ◽  
Lack Of Information ◽  
Recognition Errors ◽  
Future Work ◽  
Current Systems

Current systems with spoken language interfaces do not leverage contextual information. Therefore, they struggle with understanding speakers’ intentions. We propose a system that creates a context model from user utterances to overcome this lack of information. It comprises eight types of contextual information organized in three layers: individual, conceptual, and hierarchical. We have implemented our approach as a part of the project PARSE. It aims at enabling laypersons to construct simple programs by dialog. Our implementation incrementally generates context including occurring entities and actions as well as their conceptualizations, state transitions, and other types of contextual information. Its analyses are knowledge- or rule-based (depending on the context type), but we make use of many well-known probabilistic NLP techniques. In a user study we have shown the feasibility of our approach, achieving [Formula: see text] scores from 72% up to 98% depending on the type of contextual information. The context model enables us to resolve complex identity relations. However, quantifying this effect is subject to future work. Likewise, we plan to investigate whether our context model is useful for other language understanding tasks, e.g. anaphora resolution, topic analysis, or correction of automatic speech recognition errors.

scholarly journals Possibilitando o Reconhecimento de Expressões Faciais em Aplicações Ginga-NCL

2020 ◽  
Author(s):  
Pedro Alves Valentim ◽  
Fábio Barreto ◽  
Débora C. Muchaluat-Saade
Keyword(s):  
Facial Expression Recognition ◽  
State Of The Art ◽  
Digital Tv ◽  
Recognition Algorithm ◽  
Research Field ◽  
Expression Recognition ◽  
Proof Of Concept ◽  
Extended Version ◽  
Current Version ◽  
Current State

As the facial recognition research field grows, so do the possibilities for digital TV applications. However, in the current state of the art, it is not safe to assume there is a certain algorithm that would be the best for all kinds of applications. This work proposes an architecture to enable facial expression recognition for TV in a way that is agnostic to the recognition algorithm. As proof of concept, the proposal was developed for the Ginga middleware. There are two implementations: the first one, based on the current version of the Ginga middleware, and the second one, based on a proposed extended version of the middleware, exploring the viability of the present work.

scholarly journals Deep Bucket Elimination

2021 ◽  
Author(s):  
Yasaman Razeghi ◽  
Kalev Kask ◽  
Yadong Lu ◽  
Pierre Baldi ◽  
Sakshi Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Partition Function ◽  
Graphical Models ◽  
State Of The Art ◽  
Proof Of Concept ◽  
Accurate Approximation ◽  
Current State

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE. The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function. We provide a proof-of-concept empirically using instances from several different benchmarks, showing that DBE can be a more accurate approximation than current state-of-the-art approaches for approximating BE (e.g. the mini-bucket schemes), especially when problems are sufficiently hard.

Automatic Detection of Emotion in Music

2009 ◽  
pp. 9-33 ◽  
Author(s):  
Cyril Laurier ◽  
Perfecto Herrera
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Automatic Detection ◽  
Machine Learning Method ◽  
Future Trends ◽  
Learning Method ◽  
Proof Of Concept ◽  
Current State ◽  
Emotions In Music

Creating emotionally sensitive machines will significantly enhance the interaction between humans and machines. In this chapter we focus on enabling this ability for music. Music is extremely powerful to induce emotions. If machines can somehow apprehend emotions in music, it gives them a relevant competence to communicate with humans. In this chapter we review the theories of music and emotions. We detail different representations of musical emotions from the literature, together with related musical features. Then, we focus on techniques to detect the emotion in music from audio content. As a proof of concept, we detail a machine learning method to build such a system. We also review the current state of the art results, provide evaluations and give some insights into the possible applications and future trends of these techniques.

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