Matches in ScholarlyData for { ?s ?p ?o. }
- 334 sameAs 334.
- 334 abstract "This paper addresses a specific case of the task of lexical acquisition understood as the induction of information about the linguistic characteristics of lexical items on the basis of information gathered from their occurrences in texts. Most of the recent works in the area of lexical acquisition have used methods that take as much textual data as possible as source of evidence, but their performance decreases notably when only few occurrences of a word are available. The importance of covering such low frequency items lies in the fact that a large quantity of the words in any particular collection of texts will be occurring few times, if not just once. Our work proposes to compensate the lack of information resorting to linguistic knowledge on the characteristics of lexical classes. This knowledge, obtained from a lexical typology, is formulated probabilistically to be used in a Bayesian method to maximize the information gathered from single occurrences as to predict the full set of characteristics of the word. Our results show that our method achieves better results than others for the treatment of low frequency items.".
- 334 hasAuthorList authorList.
- 334 hasTopic Linguistics.
- 334 isPartOf proceedings.
- 334 keyword "Acquisition, Machine Learning".
- 334 keyword "Lexicon, lexical database".
- 334 title "Automatic Acquisition for low frequency lexical items".
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- item-1 hasContent nuria-bel.
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- 335 creator tomanek-katrin.
- 335 creator udo-hahn.
- 335 type InProceedings.
- 335 label "Approximating Learning Curves for Active-Learning-Driven Annotation".
- 335 sameAs 335.
- 335 abstract "Active learning (AL) is getting more and more popular as a methodology to considerably reduce the annotation effort when building training material for statistical learning methods for various NLP tasks. A crucial issue rarely addressed, however, is when to actually stop the annotation process to profit from the savings in efforts. This question is tightly related to estimating the classifier performance after a certain amount of data has already been annotated. While learning curves are the default means to monitor the progress of the annotation process in terms of classifier performance, this requires a labeled gold standard which - in realistic annotation settings, at least - is often unavailable. We here propose a method for committee-based AL to approximate the progression of the learning curve based on the disagreement among the committee members. This method relies on a separate, unlabeled corpus and is thus well suited for situations where a labeled gold standard is not available or would be too expensive to obtain. Considering named entity recognition as a test case we provide empirical evidence that this approach works well under simulation as well as under real-world annotation conditions.".
- 335 hasAuthorList authorList.
- 335 hasTopic Linguistics.
- 335 isPartOf proceedings.
- 335 keyword "Acquisition, Machine Learning".
- 335 keyword "Corpus (creation, annotation, etc.)".
- 335 title "Approximating Learning Curves for Active-Learning-Driven Annotation".
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- item-1 hasContent tomanek-katrin.
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- 336 creator friedhelm-schwenker.
- 336 creator guenther-palm.
- 336 creator hansjoerg-hofmann.
- 336 creator malte-lampmann.
- 336 creator martin-pfeil.
- 336 creator stefan-scherer.
- 336 creator steffen-rhinow.
- 336 type InProceedings.
- 336 label "Emotion Recognition from Speech: Stress Experiment".
- 336 sameAs 336.
- 336 abstract "The goal of this work is to introduce an architecture to automatically detect the amount of stress in the speech signal close to real time. For this an experimental setup to record speech rich in vocabulary and containing different stress levels is presented. Additionally, an experiment explaining the labeling process with a thorough analysis of the labeled data is presented. Fifteen subjects were asked to play an air controller simulation that gradually induced more stress by becoming more difficult to control. During this game the subjects were asked to answer questions, which were then labeled by a different set of subjects in order to receive a subjective target value for each of the answers. A recurrent neural network was used to measure the amount of stress contained in the utterances after training. The neural network estimated the amount of stress at a frequency of 25 Hz and outperformed the human baseline.".
- 336 hasAuthorList authorList.
- 336 hasTopic Linguistics.
- 336 isPartOf proceedings.
- 336 keyword "Emotions".
- 336 keyword "Speech recognition and understanding".
- 336 keyword "Speech resource/database".
- 336 title "Emotion Recognition from Speech: Stress Experiment".
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- item-1 hasContent stefan-scherer.
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