Entity Linking for the Semantic Annotation of Italian Tweets
Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which expl...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Accademia University Press
2016-06-01
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| Series: | IJCoL |
| Online Access: | https://journals.openedition.org/ijcol/362 |
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| _version_ | 1850124167632912384 |
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| author | Pierpaolo Basile Giovanni Semeraro Annalina Caputo |
| author_facet | Pierpaolo Basile Giovanni Semeraro Annalina Caputo |
| author_sort | Pierpaolo Basile |
| collection | DOAJ |
| description | Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which exploits the similarity measure computed over a Distributional Semantic Model, in the context of Italian tweets. In order to evaluate the proposed algorithm, we introduce a new dataset of tweets for entity linking that we have developed specifically for the Italian language. |
| format | Article |
| id | doaj-art-16789d0e7f804dcb8672b74fdf8edb8a |
| institution | OA Journals |
| issn | 2499-4553 |
| language | English |
| publishDate | 2016-06-01 |
| publisher | Accademia University Press |
| record_format | Article |
| series | IJCoL |
| spelling | doaj-art-16789d0e7f804dcb8672b74fdf8edb8a2025-08-20T02:34:24ZengAccademia University PressIJCoL2499-45532016-06-0121879910.4000/ijcol.362Entity Linking for the Semantic Annotation of Italian TweetsPierpaolo BasileGiovanni SemeraroAnnalina CaputoLinking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which exploits the similarity measure computed over a Distributional Semantic Model, in the context of Italian tweets. In order to evaluate the proposed algorithm, we introduce a new dataset of tweets for entity linking that we have developed specifically for the Italian language.https://journals.openedition.org/ijcol/362 |
| spellingShingle | Pierpaolo Basile Giovanni Semeraro Annalina Caputo Entity Linking for the Semantic Annotation of Italian Tweets IJCoL |
| title | Entity Linking for the Semantic Annotation of Italian Tweets |
| title_full | Entity Linking for the Semantic Annotation of Italian Tweets |
| title_fullStr | Entity Linking for the Semantic Annotation of Italian Tweets |
| title_full_unstemmed | Entity Linking for the Semantic Annotation of Italian Tweets |
| title_short | Entity Linking for the Semantic Annotation of Italian Tweets |
| title_sort | entity linking for the semantic annotation of italian tweets |
| url | https://journals.openedition.org/ijcol/362 |
| work_keys_str_mv | AT pierpaolobasile entitylinkingforthesemanticannotationofitaliantweets AT giovannisemeraro entitylinkingforthesemanticannotationofitaliantweets AT annalinacaputo entitylinkingforthesemanticannotationofitaliantweets |