Case Contribution in Example-Based Verb Sense Disambiguation

Fujii, Atsushi; Inui, Kentaro; Tokunaga, Takenobu; Hozumi, Takeshi

doi:10.5715/jnlp.4.2_111

Cited by 38 publications

(50 citation statements)

References 6 publications

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“…It means that data samples arrive in a stream and the learner has to decide, on a sample per sample basis, if it wants the current sample to be labeled [29] [30] [31]. This is definitely the problem we face in gesture command learning: to decide after each gesture command if it will be interesting to learn from this gesture, and then ask the user for its true label.…”

Section: Active Learningmentioning

confidence: 99%

Online active supervision of an evolving classifier for customized-gesture-command learning

Bouillon

Anquetil

2017

Neurocomputing

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To cite this version:Manuel Bouillon, Eric Anquetil.Online active supervision of an evolving classifier for customized-gesture-command learning.Neurocomputing AbstractTouch sensitive interfaces enable new interaction methods, like gesture commands. For users to easily memorize more than a dozen of gesture commands, it is important to enable gesture set customization. The classifier used to recognize drawn symbols must hence be customizable -able to learn from very few data -and evolving -able to learn new classes on-the-fly and improve during its use. The objective of this work is to obtain a gesture command system that cooperates as best as possible with the user: that learns from its mistakes without soliciting the user too often. This paper presents a novel approach for the online active learning of gesture commands, with three contributions. The IntuiSup supervisor monitors the learning process and user interactions. The Evolving Sampling by Uncertainty (ESU) algorithm enables to maintain the error/interaction compromise over time. The Boosted-ESU (B-ESU) method optimizes interaction impact to fasten system learning speed. The efficiency of our approach is evaluated on the publicly available ILG Data Base of gesture commands. Experimentation shows the effectiveness of the supervision strategy and improvements both in term of accuracy and learning speed.

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Section: Active Learningmentioning

confidence: 99%

Online active supervision of an evolving classifier for customized-gesture-command learning

Bouillon

Anquetil

2017

Neurocomputing

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“…In the literature, there are abundant stream-based active learning methods. The committee-based sampling method proposed in [15] constructs a 'committee' of classifiers and queries the items which are most disagreed upon by the committee members; In [62], the active learner queries the items that are most difficult to classify in order to revise the decision boundary; [22] proposes to query items which correctly disambiguate as much unseen data as possible. Although there are many active learning methods, most of them assume that all the classes to be learned are known before learning starts, and the goal is to find the query data that are "best" for improving the accuracy of the classifiers.…”

Section: Related Workmentioning

confidence: 99%

A general framework for never-ending learning from time series streams

Chen

Hao

Rakthanmanon

et al. 2014

Data Min Knowl Disc

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Abstract-Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments that consider real-world problems in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

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“…In one of the few works directly applied to WSD, Fujii, Inui, Tokunaga, and Tanaka (1998) used selective sampling for the acquisition of examples for the disambiguation of verb senses, in an iterative process with human taggers. The informative examples were chosen following two criteria: maximum number of neighbors in unsupervised data, and minimum similarity with the supervised example set.…”

Section: Related Workmentioning

confidence: 99%

On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation

Martínez¹,

Lacalle²,

Agirre³

2008

jair

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This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million examples from Web snippets for all polysemous nouns in WordNet. This paper focuses on the issues that arise when using those examples, all alone or in addition to manually tagged examples, to train a supervised WSD system for all nouns. The extensive evaluation on both lexical-sample and all-words Senseval benchmarks shows that we are able to improve over commonly used baselines and to achieve top-rank performance. The good use of the prior distributions from the senses proved to be a crucial factor.

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Case Contribution in Example-Based Verb Sense Disambiguation

Cited by 38 publications

References 6 publications

Online active supervision of an evolving classifier for customized-gesture-command learning

Online active supervision of an evolving classifier for customized-gesture-command learning

A general framework for never-ending learning from time series streams

On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation

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