Opinion aspect extraction in Dutch childrens diary entries
Aspect extraction can be used in dialogue systems to understand the topic of opinionated text. Expressing an empathetic reaction to an opinion can strengthen the bond between a human and, for example, a robot. The aim of this study is three-fold: 1. create a new annotated dataset for both aspect ext...
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| creator | Haanstra, Hella de Boer, Maaike H. T |
| description | Aspect extraction can be used in dialogue systems to understand the topic of
opinionated text. Expressing an empathetic reaction to an opinion can
strengthen the bond between a human and, for example, a robot. The aim of this
study is three-fold: 1. create a new annotated dataset for both aspect
extraction and opinion words for Dutch childrens language, 2. acquire aspect
extraction results for this task and 3. improve current results for aspect
extraction in Dutch reviews. This was done by training a deep learning Gated
Recurrent Unit (GRU) model, originally developed for an English review dataset,
on Dutch restaurant review data to classify both opinion words and their
respective aspects. We obtained state-of-the-art performance on the Dutch
restaurant review dataset. Additionally, we acquired aspect extraction results
for the Dutch childrens dataset. Since the model was trained on standardised
language, these results are quite promising. |
| doi_str_mv | 10.48550/arxiv.1910.10502 |
| format | Article |
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opinionated text. Expressing an empathetic reaction to an opinion can
strengthen the bond between a human and, for example, a robot. The aim of this
study is three-fold: 1. create a new annotated dataset for both aspect
extraction and opinion words for Dutch childrens language, 2. acquire aspect
extraction results for this task and 3. improve current results for aspect
extraction in Dutch reviews. This was done by training a deep learning Gated
Recurrent Unit (GRU) model, originally developed for an English review dataset,
on Dutch restaurant review data to classify both opinion words and their
respective aspects. We obtained state-of-the-art performance on the Dutch
restaurant review dataset. Additionally, we acquired aspect extraction results
for the Dutch childrens dataset. Since the model was trained on standardised
language, these results are quite promising.</description><identifier>DOI: 10.48550/arxiv.1910.10502</identifier><language>eng</language><subject>Computer Science - Computation and Language ; Computer Science - Learning</subject><creationdate>2019-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1910.10502$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1910.10502$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Haanstra, Hella</creatorcontrib><creatorcontrib>de Boer, Maaike H. T</creatorcontrib><title>Opinion aspect extraction in Dutch childrens diary entries</title><description>Aspect extraction can be used in dialogue systems to understand the topic of
opinionated text. Expressing an empathetic reaction to an opinion can
strengthen the bond between a human and, for example, a robot. The aim of this
study is three-fold: 1. create a new annotated dataset for both aspect
extraction and opinion words for Dutch childrens language, 2. acquire aspect
extraction results for this task and 3. improve current results for aspect
extraction in Dutch reviews. This was done by training a deep learning Gated
Recurrent Unit (GRU) model, originally developed for an English review dataset,
on Dutch restaurant review data to classify both opinion words and their
respective aspects. We obtained state-of-the-art performance on the Dutch
restaurant review dataset. Additionally, we acquired aspect extraction results
for the Dutch childrens dataset. Since the model was trained on standardised
language, these results are quite promising.</description><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7tOAzEQRd1QoCQfQIV_YMPYXj82HQpPKVKa9KthdqxYCmblNSj8fR5QXekUV-cIcadg2QZr4QHLMf0sVXcGCizoW7HajimnryxxGpmq5GMtSPVCUpZP35X2kvbpMBTOkxwSll_JuZbE01zcRDxMvPjfmdi9PO_Wb81m-_q-ftw06LxuCAF8Z0ir1jjwSNZiNK2yXkcCdPzhVacRyATNgck46oYQlVXRBd2CmYn7v9urfD-W9HmW6C8R_TXCnAA0cUEi</recordid><startdate>20191021</startdate><enddate>20191021</enddate><creator>Haanstra, Hella</creator><creator>de Boer, Maaike H. T</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20191021</creationdate><title>Opinion aspect extraction in Dutch childrens diary entries</title><author>Haanstra, Hella ; de Boer, Maaike H. T</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-ca00793c2143607ac55af341572fc0a6eb7192a0c382e8ec36c9d8f151f682403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Haanstra, Hella</creatorcontrib><creatorcontrib>de Boer, Maaike H. T</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Haanstra, Hella</au><au>de Boer, Maaike H. T</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Opinion aspect extraction in Dutch childrens diary entries</atitle><date>2019-10-21</date><risdate>2019</risdate><abstract>Aspect extraction can be used in dialogue systems to understand the topic of
opinionated text. Expressing an empathetic reaction to an opinion can
strengthen the bond between a human and, for example, a robot. The aim of this
study is three-fold: 1. create a new annotated dataset for both aspect
extraction and opinion words for Dutch childrens language, 2. acquire aspect
extraction results for this task and 3. improve current results for aspect
extraction in Dutch reviews. This was done by training a deep learning Gated
Recurrent Unit (GRU) model, originally developed for an English review dataset,
on Dutch restaurant review data to classify both opinion words and their
respective aspects. We obtained state-of-the-art performance on the Dutch
restaurant review dataset. Additionally, we acquired aspect extraction results
for the Dutch childrens dataset. Since the model was trained on standardised
language, these results are quite promising.</abstract><doi>10.48550/arxiv.1910.10502</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language Computer Science - Learning |
| title | Opinion aspect extraction in Dutch childrens diary entries |
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