scholarly journals Numerical Concepts in Context

2021 ◽  
pp. 93-119
Author(s):  
Paola Gega ◽  
Mingya Liu ◽  
Lucas Bechberger
Keyword(s):  
Natural Language ◽  
Real World ◽  
Linguistic Study ◽  
Corpus Linguistic ◽  
Contextual Variability ◽  
Precise Interpretation

AbstractNumerical concepts are an integral part of everyday conversation and communication. Expressions relating to numbers in natural language can have precise or imprecise interpretations. While the precise interpretation most prominently appears in mathematical contexts, the imprecise interpretation seems to arise when numbers (as quantities) are applied to real world contexts (e.g., the rope is 50 m long). Earlier literature shows that the (im)precise interpretation can depend on different factors, e.g., the kind of approximator a numeral appears with (precise vs. imprecise, e.g., exactly vs. roughly) or the kind of numeral itself (round vs. non-round, e.g., 50 vs. 47). We report on a corpus-linguistic study and a rating experiment of English numerical expressions. The results confirm the effects of both factors and additionally an effect of the kind of unit (discrete vs. continuous, e.g., people vs. meters). This shows the contextual variability in the interpretation of numerical concepts in natural language.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

scholarly journals Robust Processing of Real-World Natural-Language Texts

1991 ◽  
Author(s):  
Jerry R. Hobbs ◽  
Douglas E. Appelt ◽  
John Bear ◽  
Mabry Tyson ◽  
David Magerman
Keyword(s):  
Natural Language ◽  
Real World ◽  
Robust Processing

Discovering Opinions from Customers' Unstructured Textual Reviews Written in Different Natural Languages

2014 ◽  
pp. 834-859
Author(s):  
Jan Žižka ◽  
František Dařena
Keyword(s):  
Decision Tree ◽  
Natural Language ◽  
Real World ◽  
Large Data ◽  
Main Question ◽  
Internet Shopping ◽  
Natural Languages ◽  
On Line ◽  
Multilingual Data ◽  
Textual Form

Gaining new and keeping existing clients or customers can be well-supported by creating and monitoring feedbacks: “Are the customers satisfied? Can we improve our services?” One of possible feedbacks is allowing the customers to freely write their reviews using a simple textual form. The more reviews that are available, the better knowledge can be acquired and applied to improving the service. However, very large data generated by collecting the reviews has to be processed automatically as humans usually cannot manage it within an acceptable time. The main question is “Can a computer reveal an opinion core hidden in text reviews?” It is a challenging task because the text is written in a natural language. This chapter presents a method based on the automatic extraction of expressions that are significant for specifying a review attitude to a given topic. The significant expressions are composed using significant words revealed in the documents. The significant words are selected by a decision-tree generator based on the entropy minimization. Words included in branches represent kernels of the significant expressions. The full expressions are composed of the significant words and words surrounding them in the original documents. The results are here demonstrated using large real-world multilingual data representing customers' opinions concerning hotel accommodation booked on-line, and Internet shopping. Knowledge discovered in the reviews may subsequently serve for various marketing tasks.

How to Catch an AI Zombie

2020 ◽  
pp. 439-458
Author(s):  
Susan Schneider
Keyword(s):  
Information Theory ◽  
Natural Language ◽  
Real World ◽  
Phenomenal Consciousness ◽  
Artificial Consciousness ◽  
High Level ◽  
Integrated Information Theory ◽  
Integrated Information

How can we determine if AI is conscious? The chapter begins by illustrating that there are potentially very serious real-world costs to getting facts about AI consciousness wrong. It then proposes a provisional framework for investigating artificial consciousness that involves several tests or markers. One test is the AI Consciousness Test, which challenges an AI with a series of increasingly demanding natural-language interactions. Another test is based on the Integrated Information Theory, developed by Giulio Tononi and others, and considers whether a machine has a high level of “integrated information.” A third test is a Chip Test, where speculatively an individual’s brain is gradually replaced with durable microchips. If this individual being tested continues to report having phenomenal consciousness, the chapter argues that this could be a reason to believe that some machines could have consciousness.

Parsing

2012 ◽  
Author(s):  
John Carroll
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Level Of Detail ◽  
Semantic Interpretation ◽  
Syntactic Analysis ◽  
Real World Applications ◽  
Grammar Formalisms ◽  
Speech Recognizer ◽  
Context Free

This article introduces the concepts and techniques for natural language (NL) parsing, which signifies, using a grammar to assign a syntactic analysis to a string of words, a lattice of word hypotheses output by a speech recognizer or similar. The level of detail required depends on the language processing task being performed and the particular approach to the task that is being pursued. This article further describes approaches that produce ‘shallow’ analyses. It also outlines approaches to parsing that analyse the input in terms of labelled dependencies between words. Producing hierarchical phrase structure requires grammars that have at least context-free (CF) power. CF algorithms that are widely used in parsing of NL are described in this article. To support detailed semantic interpretation more powerful grammar formalisms are required, but these are usually parsed using extensions of CF parsing algorithms. Furthermore, this article describes unification-based parsing. Finally, it discusses three important issues that have to be tackled in real-world applications of parsing: evaluation of parser accuracy, parser efficiency, and measurement of grammar/parser coverage.

Mining information from sentences through Semantic Web data and Information Extraction tasks

2020 ◽  
pp. 016555152093438
Author(s):  
Jose L. Martinez-Rodriguez ◽  
Ivan Lopez-Arevalo ◽  
Ana B. Rios-Alvarado
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Information Extraction ◽  
Knowledge Base ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Real World ◽  
Semantic Similarity Measure ◽  
Web Standards ◽  
Extract Information

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.

scholarly journals A Structured Review of the Validity of BLEU

2018 ◽  
Vol 44 (3) ◽  
pp. 393-401 ◽  
Author(s):  
Ehud Reiter
Keyword(s):  
Natural Language ◽  
Hypothesis Testing ◽  
Machine Translation ◽  
Real World ◽  
User Satisfaction ◽  
Natural Language Generation ◽  
Diagnostic Evaluation ◽  
Evaluation Technique ◽  
Language Generation ◽  
Scientific Hypothesis

The BLEU metric has been widely used in NLP for over 15 years to evaluate NLP systems, especially in machine translation and natural language generation. I present a structured review of the evidence on whether BLEU is a valid evaluation technique—in other words, whether BLEU scores correlate with real-world utility and user-satisfaction of NLP systems; this review covers 284 correlations reported in 34 papers. Overall, the evidence supports using BLEU for diagnostic evaluation of MT systems (which is what it was originally proposed for), but does not support using BLEU outside of MT, for evaluation of individual texts, or for scientific hypothesis testing.

scholarly journals Vocabulary Alignment for Collaborative Agents: a Study with Real-World Multilingual How-to Instructions

2018 ◽  
Author(s):  
Paula Chocron ◽  
Paolo Pareti
Keyword(s):  
Natural Language ◽  
Real World ◽  
Heterogeneous Agents ◽  
External Resource ◽  
Open Environments

Collaboration between heterogeneous agents typically requires the ability to communicate meaningfully. This can be challenging in open environments where participants may use different languages. Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks  being executed, without any external resource. Until now, this approach has only been evaluated with artificially created data. We adapt this technique to protocols written by humans in natural language, which we extract from instructional webpages. In doing so, we show how to take into account challenges that arise when working with natural language labels.The quality of the alignments obtained with our technique is evaluated in terms of their effectiveness in enabling successful collaborations, using a translation dictionary as a baseline. We show how our technique outperforms the dictionary when used to interact.

Sign in / Sign up

Export Citation Format

Share Document