@inproceedings{petersen-hellwig-2016-exploring,
title = "Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in {G}erman",
author = "Petersen, Wiebke and
Hellwig, Oliver",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1267",
pages = "2839--2848",
abstract = "The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="petersen-hellwig-2016-exploring">
<titleInfo>
<title>Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wiebke</namePart>
<namePart type="family">Petersen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oliver</namePart>
<namePart type="family">Hellwig</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2016-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yuji</namePart>
<namePart type="family">Matsumoto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rashmi</namePart>
<namePart type="family">Prasad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>The COLING 2016 Organizing Committee</publisher>
<place>
<placeTerm type="text">Osaka, Japan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.</abstract>
<identifier type="citekey">petersen-hellwig-2016-exploring</identifier>
<location>
<url>https://aclanthology.org/C16-1267</url>
</location>
<part>
<date>2016-12</date>
<extent unit="page">
<start>2839</start>
<end>2848</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German
%A Petersen, Wiebke
%A Hellwig, Oliver
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F petersen-hellwig-2016-exploring
%X The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.
%U https://aclanthology.org/C16-1267
%P 2839-2848
Markdown (Informal)
[Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German](https://aclanthology.org/C16-1267) (Petersen & Hellwig, COLING 2016)
ACL