What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News
Local news articles are a subset of news that impact users in a geographical area, such as a city, county, or state. Detecting local news (Step 1) and subsequently deciding its geographical location as well as radius of impact (Step 2) are two important steps towards accurate local news recommendati...
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| creator | Shah, Deven Santosh He, Shiying Siddiqi, Gosuddin Kamaruddin Bansal, Radhika |
| description | Local news articles are a subset of news that impact users in a geographical
area, such as a city, county, or state. Detecting local news (Step 1) and
subsequently deciding its geographical location as well as radius of impact
(Step 2) are two important steps towards accurate local news recommendation.
Naive rule-based methods, such as detecting city names from the news title,
tend to give erroneous results due to lack of understanding of the news
content. Empowered by the latest development in natural language processing, we
develop an integrated pipeline that enables automatic local news detection and
content-based local news recommendations. In this paper, we focus on Step 1 of
the pipeline, which highlights: (1) a weakly supervised framework incorporated
with domain knowledge and auto data processing, and (2) scalability to
multi-lingual settings. Compared with Stanford CoreNLP NER model, our pipeline
has higher precision and recall evaluated on a real-world and human-labeled
dataset. This pipeline has potential to more precise local news to users, helps
local businesses get more exposure, and gives people more information about
their neighborhood safety. |
| doi_str_mv | 10.48550/arxiv.2301.08146 |
| format | Article |
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area, such as a city, county, or state. Detecting local news (Step 1) and
subsequently deciding its geographical location as well as radius of impact
(Step 2) are two important steps towards accurate local news recommendation.
Naive rule-based methods, such as detecting city names from the news title,
tend to give erroneous results due to lack of understanding of the news
content. Empowered by the latest development in natural language processing, we
develop an integrated pipeline that enables automatic local news detection and
content-based local news recommendations. In this paper, we focus on Step 1 of
the pipeline, which highlights: (1) a weakly supervised framework incorporated
with domain knowledge and auto data processing, and (2) scalability to
multi-lingual settings. Compared with Stanford CoreNLP NER model, our pipeline
has higher precision and recall evaluated on a real-world and human-labeled
dataset. This pipeline has potential to more precise local news to users, helps
local businesses get more exposure, and gives people more information about
their neighborhood safety.</description><identifier>DOI: 10.48550/arxiv.2301.08146</identifier><language>eng</language><subject>Computer Science - Computation and Language ; Computer Science - Information Retrieval ; Computer Science - Learning</subject><creationdate>2023-01</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2301.08146$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2301.08146$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Shah, Deven Santosh</creatorcontrib><creatorcontrib>He, Shiying</creatorcontrib><creatorcontrib>Siddiqi, Gosuddin Kamaruddin</creatorcontrib><creatorcontrib>Bansal, Radhika</creatorcontrib><title>What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News</title><description>Local news articles are a subset of news that impact users in a geographical
area, such as a city, county, or state. Detecting local news (Step 1) and
subsequently deciding its geographical location as well as radius of impact
(Step 2) are two important steps towards accurate local news recommendation.
Naive rule-based methods, such as detecting city names from the news title,
tend to give erroneous results due to lack of understanding of the news
content. Empowered by the latest development in natural language processing, we
develop an integrated pipeline that enables automatic local news detection and
content-based local news recommendations. In this paper, we focus on Step 1 of
the pipeline, which highlights: (1) a weakly supervised framework incorporated
with domain knowledge and auto data processing, and (2) scalability to
multi-lingual settings. Compared with Stanford CoreNLP NER model, our pipeline
has higher precision and recall evaluated on a real-world and human-labeled
dataset. This pipeline has potential to more precise local news to users, helps
local businesses get more exposure, and gives people more information about
their neighborhood safety.</description><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Information Retrieval</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7tOwzAUgGEvDKjwAEycjSnBji9pJhSVW6UIBiqVLTqxTxqLEEdOWujbIwrTv_3Sx9iV4Klaas1vMX77Q5pJLlK-FMqcs_dth_PNBB2OIw1-2IEf4Bj2EQbyu64JsQvB3UEJW8KP_ghv-5HiwU_koBzHGNB2MAe4p5nsDFWw2MMLfU0X7KzFfqLL_y7Y5vFhs3pOqten9aqsEjS5SQoikzlheKOIMse1dLniUqlcIWY5GbTC2UKQFUVGJChvdYuqUYXUxDWXC3b9tz3R6jH6T4zH-pdYn4jyB65STAc</recordid><startdate>20230114</startdate><enddate>20230114</enddate><creator>Shah, Deven Santosh</creator><creator>He, Shiying</creator><creator>Siddiqi, Gosuddin Kamaruddin</creator><creator>Bansal, Radhika</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230114</creationdate><title>What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News</title><author>Shah, Deven Santosh ; He, Shiying ; Siddiqi, Gosuddin Kamaruddin ; Bansal, Radhika</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-9ee62d160b4ee2d053d74034474aa27e6ac1dc91ec192ee1e7f5fa4b4935e0503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Information Retrieval</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Shah, Deven Santosh</creatorcontrib><creatorcontrib>He, Shiying</creatorcontrib><creatorcontrib>Siddiqi, Gosuddin Kamaruddin</creatorcontrib><creatorcontrib>Bansal, Radhika</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shah, Deven Santosh</au><au>He, Shiying</au><au>Siddiqi, Gosuddin Kamaruddin</au><au>Bansal, Radhika</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News</atitle><date>2023-01-14</date><risdate>2023</risdate><abstract>Local news articles are a subset of news that impact users in a geographical
area, such as a city, county, or state. Detecting local news (Step 1) and
subsequently deciding its geographical location as well as radius of impact
(Step 2) are two important steps towards accurate local news recommendation.
Naive rule-based methods, such as detecting city names from the news title,
tend to give erroneous results due to lack of understanding of the news
content. Empowered by the latest development in natural language processing, we
develop an integrated pipeline that enables automatic local news detection and
content-based local news recommendations. In this paper, we focus on Step 1 of
the pipeline, which highlights: (1) a weakly supervised framework incorporated
with domain knowledge and auto data processing, and (2) scalability to
multi-lingual settings. Compared with Stanford CoreNLP NER model, our pipeline
has higher precision and recall evaluated on a real-world and human-labeled
dataset. This pipeline has potential to more precise local news to users, helps
local businesses get more exposure, and gives people more information about
their neighborhood safety.</abstract><doi>10.48550/arxiv.2301.08146</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Learning |
| title | What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News |
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