{
 "id": "omilab/hebrew_sentiment",
 "owner": "omilab",
 "name": "hebrew_sentiment",
 "downloads": 238,
 "likes": 8,
 "last_modified": "2024-01-18T11:05:19.000Z",
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 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "@inproceedings{amram-etal-2018-representations,\n    title = \"Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew\",\n    author = \"Amram, Adam  and\n      Ben David, Anat  and\n      Tsarfaty, Reut\",\n    booktitle = \"Proceedings of the 27th International Conference on Computational Linguistics\",\n    month = aug,\n    year = \"2018\",\n    address = \"Santa Fe, New Mexico, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/C18-1190\",\n    pages = \"2242--2252\",\n    abstract = \"This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to t",
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}