scholarly journals Did It Happen? The Pragmatic Complexity of Veridicality Assessment

2012 ◽  
Vol 38 (2) ◽  
pp. 301-333 ◽  
Author(s):  
Marie-Catherine de Marneffe ◽  
Christopher D. Manning ◽  
Christopher Potts
Keyword(s):  
Natural Language ◽  
Significant Role ◽  
Structural Features ◽  
Event Extraction ◽  
World Knowledge ◽  
Language Understanding ◽  
Lexical Semantic ◽  
Computational Work ◽  
Will Rogers ◽  
Semantic Properties

Natural language understanding depends heavily on assessing veridicality—whether events mentioned in a text are viewed as happening or not—but little consideration is given to this property in current relation and event extraction systems. Furthermore, the work that has been done has generally assumed that veridicality can be captured by lexical semantic properties whereas we show that context and world knowledge play a significant role in shaping veridicality. We extend the FactBank corpus, which contains semantically driven veridicality annotations, with pragmatically informed ones. Our annotations are more complex than the lexical assumption predicts but systematic enough to be included in computational work on textual understanding. They also indicate that veridicality judgments are not always categorical, and should therefore be modeled as distributions. We build a classifier to automatically assign event veridicality distributions based on our new annotations. The classifier relies not only on lexical features like hedges or negations, but also on structural features and approximations of world knowledge, thereby providing a nuanced picture of the diverse factors that shape veridicality. “All I know is what I read in the papers” —Will Rogers

A WORD-BASED CHINESE LANGUAGE UNDERSTANDING SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 25-35
Author(s):  
TIAN-SHUN YAO
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Computer Programs ◽  
World Knowledge ◽  
Knowledge Source ◽  
Language Understanding ◽  
Language Analysis ◽  
The World

With the word-based theory of natural language processing, a word-based Chinese language understanding system has been developed. In the light of psychological language analysis and the features of the Chinese language, this theory of natural language processing is presented with the description of the computer programs based on it. The heart of the system is to define a Total Information Dictionary and the World Knowledge Source used in the system. The purpose of this research is to develop a system which can understand not only Chinese sentences but also the whole text.

Information Extraction from Text and Beyond

2012 ◽  
pp. 24-39
Author(s):  
Marie-Francine Moens
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Natural Language Understanding ◽  
World Knowledge ◽  
Language Understanding ◽  
Common Information ◽  
Current Information ◽  
Build Systems

In this chapter, the author defines information extraction from text, describes common information extraction tasks, and discusses current information extraction issues being the need to develop technologies that require a minimum of human supervision, to build systems that automatically acquire world knowledge, and to integrate their outputs into advanced information extraction systems. Current emerging research on extraction of narrative scenarios and complex concepts revives an old dream and opens a way to full natural language understanding.

scholarly journals Natural Language Understanding: Methodological Conceptualization

2019 ◽  
Vol 25 (1) ◽  
pp. 431-443
Author(s):  
Vitalii Shymko
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Structural Features ◽  
Matter Field ◽  
Language Understanding ◽  
Composite Function ◽  
Motivational Influences ◽  
The One ◽  
The Mind

This article contains the results of a theoretical analysis of the phenomenon of natural language understanding (NLU), as a methodological problem. The combination of structural-ontological and informational-psychological approaches provided an opportunity to describe the subject matter field of NLU, as a composite function of the mind, which systemically combines the verbal and discursive structural layers. In particular, the idea of NLU is presented, on the one hand, as the relation between the discourse of a specific speech message and the meta-discourse of a language, in turn, activated by the need-motivational factors. On the other hand, it is conceptualized as a process with a specific structure of information metabolism, the study of which implies the necessity to differentiate the affective (emotional) and need-motivational influences on the NLU, as well as to take into account their interaction. At the same time, the hypothesis about the influence of needs on NLU under the scenario similar to the pattern of Yerkes-Dodson is argued. And the theoretical conclusion that emotions fulfill the function of the operator of the structural features of the information metabolism of NLU is substantiated. Thus, depending on the modality of emotions in the process of NLU, it was proposed to distinguish two scenarios for the implementation of information metabolism - reduction and synthetic. The argument in favor of the conclusion about the productive and constitutive role of emotions in the process of NLU is also given.

scholarly journals A Survey of Reinforcement Learning Informed by Natural Language

2019 ◽  
Author(s):  
Jelena Luketina ◽  
Nantas Nardelli ◽  
Gregory Farquhar ◽  
Jakob Foerster ◽  
Jacob Andreas ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Representation Learning ◽  
World Knowledge ◽  
Language Understanding ◽  
Text Corpora ◽  
The World

To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. Recent advances in representation learning for language make it possible to build models that acquire world knowledge from text corpora and integrate this knowledge into downstream decision making problems. We thus argue that the time is right to investigate a tight integration of natural language understanding into RL in particular. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge. Finally, we call for the development of new environments as well as further investigation into the potential uses of recent Natural Language Processing (NLP) techniques for such tasks.

scholarly journals The Taboo Challenge Competition

AI Magazine ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 84-87 ◽  
Author(s):  
Michael Rovatsos ◽  
Dagmar Gromann ◽  
Gábor Bella
Keyword(s):  
Decision Making ◽  
Natural Language ◽  
Domain Knowledge ◽  
Lessons Learned ◽  
Board Game ◽  
World Knowledge ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
First Time ◽  
Level Performance

Games have always been a popular domain of AI research, and they have been used for many recent competitions. However, reaching human-level performance often either focuses on comprehensive world knowledge or solving decision-making problems with unmanageable solution spaces. Building on the popular Taboo board game, the Taboo Challenge Competition addresses a different problem — that of bridging the gap between the domain knowledge of heterogeneous agents trying to jointly identify a concept without making reference to its most salient features. The competition, which was run for the first time at IJCAI 2017, aims to provide a simple testbed for diversity-aware AI where the focus is on integrating independently engineered AI components, while offering a scenario that is challenging yet simple enough to not require mastering general commonsense knowledge or natural language understanding. We describe the design and preparation of the competition, discuss results, and lessons learned.

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

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