Spain

ECAI 2020

Improving Document Classification with Multi-Sense Embeddings

context sensitive learning

document vectors

multi-sense embedding

document embedding

scdv

sparse representation

manifold learning

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

technical paper

Reasoning with a Bounded Number of Resources in ATL+

Rewrite or not Rewrite? ML-based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

Unsupervised Adversarial Learning of Anomaly Detection in the Wild

Ensemble Methods for Object Detection

Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking

Learning to Predict Charges for Legal Judgment via Self-Attentive Capsule Network

Bidirectional Scene Text Recognition with a Single Decoder

Humans are capable of abstracting away irrelevant details or distinguish the common properties when studying problems. This is especially noticeable for planning problems, as humans are able to disregard certain parts of the domain and focus on the key elements important for finding a plan. Recently, the notion of abstraction has been introduced for Answer Set Programming (ASP), a knowledge representation and reasoning paradigm widely used in problem solving, with the potential to understand the key elements of a program that play a role in finding a solution. This paper highlights the potential use of such an abstraction in getting to the essence of planning problems, expressed in ASP, by disregarding irrelevant details and computing abstract plans.

Abstraction for ASP Planning

In some abstract argumentation framework (AAF), arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknown and sometimes there is a need for aggregating the probability values of different sources. In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise.

Towards an Imprecise Probability Approach for Abstract Argumentation

Local to Global Learning of a Latent Dynamic Bayesian Network

poster

https://d3smihljt9218e.cloudfront.net/transcript/1270/subtitle/ff4ce8329d2c528a4503e350b430c6b3.vtt

English (automatic)

Improving Document Classification with Multi-Sense Embeddings

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Reasoning with a Bounded Number of Resources in ATL+

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Improving Document Classification with Multi-Sense Embeddings

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Next from ECAI 2020

Reasoning with a Bounded Number of Resources in ATL+

Similar lecture

Dual Attention Model for Citation Recommendation

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads