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Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
Computation and Language · Submitted 13 Jun 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The quality of the constructed dictionaries is comparable to the expert ones.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Concrete/abstract words are used in a growing number of psychological and neurophysiological research. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | For a few languages, large dictionaries have been created manually. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s2 | This is a very time-consuming and costly process. | background 0.95 | 0.07 | 0.02 | asserted 1.00 |
| s3 | To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. | background 0.73 | 0.07 | 0.05 | asserted 0.99 |
| s4 | The research question that arises is how small such samples should be to do a good enough extrapolation. | gap 0.94 | 0.05 | 0.03 | unclear 0.78 |
| s5 | In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. | contribution 1.00 | 0.94 | 0.23 | asserted 1.00 |
| s6 | The method has been evaluated on a large test set for English. | method 0.68 | 0.92 | 0.05 | asserted 1.00 |
| s7 | The quality of the constructed dictionaries is comparable to the expert ones. | result 0.99 | 0.91 | 0.40 | asserted 1.00 |
| s8 | The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods. | result 1.00 | 0.89 | 0.31 | asserted 1.00 |
What was examined, and what was not
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The paper
Original abstract
Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.
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What this paper does with the arXiv papers it cites
What it cites · 3 sentences without a confident label
Uses the method or tool of 1 paper
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fastText [ref] , ELMo [ref] and BERT [ref] (for BERT-based experiments we use bert-base-uncased model and aggregate outputs of last layers of the encoder).
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · Datasets and performance measures · 0.99
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