<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sanchez-villegas-etal-2020-point">
    <titleInfo>
        <title>Point-of-Interest Type Inference from Social Media Text</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Danae</namePart>
        <namePart type="family">Sánchez Villegas</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Preotiuc-Pietro</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nikolaos</namePart>
        <namePart type="family">Aletras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kam-Fai</namePart>
            <namePart type="family">Wong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Knight</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hua</namePart>
            <namePart type="family">Wu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Suzhou, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Physical places help shape how we perceive the experiences we have there. We study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of ~200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.</abstract>
    <identifier type="citekey">sanchez-villegas-etal-2020-point</identifier>
    <identifier type="doi">10.18653/v1/2020.aacl-main.80</identifier>
    <location>
        <url>https://aclanthology.org/2020.aacl-main.80/</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>804</start>
            <end>810</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Point-of-Interest Type Inference from Social Media Text
%A Sánchez Villegas, Danae
%A Preotiuc-Pietro, Daniel
%A Aletras, Nikolaos
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F sanchez-villegas-etal-2020-point
%X Physical places help shape how we perceive the experiences we have there. We study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of ~200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.
%R 10.18653/v1/2020.aacl-main.80
%U https://aclanthology.org/2020.aacl-main.80/
%U https://doi.org/10.18653/v1/2020.aacl-main.80
%P 804-810

Markdown (Informal)

[Point-of-Interest Type Inference from Social Media Text](https://aclanthology.org/2020.aacl-main.80/) (Sánchez Villegas et al., AACL 2020)

• Point-of-Interest Type Inference from Social Media Text (Sánchez Villegas et al., AACL 2020)

ACL

• Danae Sánchez Villegas, Daniel Preotiuc-Pietro, and Nikolaos Aletras. 2020. Point-of-Interest Type Inference from Social Media Text. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 804–810, Suzhou, China. Association for Computational Linguistics.