UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43957251" target="_blank" >RIV/49777513:23520/18:43957251 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.18653/v1/S18-1153" target="_blank" >http://dx.doi.org/10.18653/v1/S18-1153</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/S18-1153" target="_blank" >10.18653/v1/S18-1153</a>

Jazyk výsledku

angličtina

Název v původním jazyce

UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions

Popis výsledku v původním jazyce

We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1% and is ranked #4 among 26 submitted systems.

Název v anglickém jazyce

UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions

Popis výsledku anglicky

We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1% and is ranked #4 among 26 submitted systems.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů