Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Rationalizing Substitutions01:29

Rationalizing Substitutions

Integrals involving non-rational functions are often difficult to evaluate using standard techniques, especially when radicals appear in the integrand. Rationalizing substitution provides a systematic method for simplifying such integrals by converting them into rational forms that are easier to handle.Consider a rod whose linear mass density depends on a constant linear density, a characteristic length, and the distance from the left end of the rod. Determining the total mass requires...
Rate-Determining Steps03:08

Rate-Determining Steps

Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
The concept of rate-determining step can be understood from the analogy of a 4-lane freeway with a short-stretch of traffic-bottleneck caused due to...
Evaluating Limits by Direct Substitution01:29

Evaluating Limits by Direct Substitution

In the analysis of functions that represent continuous physical phenomena, it is often necessary to determine the output value as the input approaches a specific point. When a combination of algebraic terms defines the function and exhibits no discontinuities or abrupt changes near the point of interest, the limit of the function can be evaluated directly. This process, known as direct substitution, involves replacing the variable in the expression with the value it approaches.Direct...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Representation of Help Givers in First Aid Instruction Materials: A Multimodal Reception Study.

Health communication·2025
Same author

Human-annotated rationales and explainable text classification: a survey.

Frontiers in artificial intelligence·2024
Same author

Interpreting vision and language generative models with semantic visual priors.

Frontiers in artificial intelligence·2023
Same author

Rethinking symbolic and visual context in Referring Expression Generation.

Frontiers in artificial intelligence·2023
Same author

Reducing Blood Transfusions in Primary Total Hip Replacement Patients: Effectiveness of Near-patient Testing and a Dedicated Preoperative Anemia Clinic.

Revista brasileira de ortopedia·2022
Same author

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy.

Cancers·2021

Related Experiment Videos

Generation of referring expressions: assessing the Incremental Algorithm.

Kees van Deemter1, Albert Gatt, Ielka van der Sluis

  • 1Computing Science Department, King's College, University of Aberdeen, Aberdeen, AB24 3UE Scotland, UK. k.vdeemter@abdn.ac.uk

Cognitive Science
|November 2, 2011
PubMed
Summary
This summary is machine-generated.

The Incremental Algorithm (IA) for generating referring expressions performs inconsistently. Its success heavily relies on the preference order (PO), making it difficult to predict human-like output for natural language generation.

Related Experiment Videos

Area of Science:

  • Natural Language Generation
  • Computational Linguistics
  • Human-Computer Interaction

Background:

  • Recent natural language generation research focuses on "one-shot" referring expressions to identify specific targets.
  • The Incremental Algorithm (IA) is widely considered optimal for generating human-like referring expressions.

Purpose of the Study:

  • To empirically test the hypothesis that the IA maximizes similarity to human-generated referring expressions.
  • To investigate the impact of the preference order (PO) on the IA's performance.

Main Methods:

  • Eliciting referring expressions from human subjects.
  • Generating referring expressions using the IA with various POs.
  • Computing similarity scores between human and algorithm-generated expressions.
  • Analyzing computational complexity of the IA and its competitors.

Main Results:

  • The IA's success is highly dependent on the chosen PO, especially in complex domains.
  • Certain POs yield high similarity to human expressions, while others perform poorly compared to competitors.
  • Predicting PO success based on psycholinguistic data or corpus frequencies is challenging.
  • No significant computational complexity advantage favors the IA over competitors.

Conclusions:

  • The IA's effectiveness is not guaranteed and is critically influenced by the PO.
  • Future research should explore alternative algorithms, such as those inspired by the Greedy Algorithm, that avoid fixed POs for improved referring expression generation.