Hierarchical Embeddings for Hypernymy Detection and Directionality
- K. Nguyen, Maximilian Köper, Sabine Schulte im Walde, Ngoc Thang Vu
- Computer ScienceConference on Empirical Methods in Natural…
- 1 July 2017
This work presents a novel neural model HyperVec, an unsupervised measure where embeddings are learned in a specific order and capture the hypernym–hyponym distributional hierarchy, which outperforms both state-of-the-art un supervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.
Experiments on the Automatic Induction of German Semantic Verb Classes
- Sabine Schulte im Walde
- LinguisticsComputational Linguistics
- 1 June 2006
This article presents clustering experiments on German verbs: A statistical grammar model for German serves as the source for a distributional verb description at the lexical syntax-semantics…
Clustering Verbs Semantically According to their Alternation Behaviour
- Sabine Schulte im Walde
- Linguistics, Computer ScienceInternational Conference on Computational…
- 31 July 2000
Verbs were clustered semantically on the basis of their alternation behaviour, as characterised by their syntactic subcategorisation frames extracted from maximum probability parses of a robust…
Chasing Hypernyms in Vector Spaces with Entropy
- Enrico Santus, Alessandro Lenci, Q. Lu, Sabine Schulte im Walde
- Computer ScienceConference of the European Chapter of the…
- 1 April 2014
SLQS is a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs).
Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change
- Dominik Schlechtweg, Sabine Schulte im Walde, S. Eckmann
- LinguisticsNorth American Chapter of the Association for…
- 18 April 2018
We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models of lexical…
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
- K. Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
- LinguisticsAnnual Meeting of the Association for…
- 25 May 2016
A novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity and integrated into the objective function of a skip-gram model is proposed.
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
- Dominik Schlechtweg, Anna Hätty, Marco Del Tredici, Sabine Schulte im Walde
- Computer ScienceAnnual Meeting of the Association for…
- 1 June 2019
This work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task.
Multilingual Reliability and “Semantic” Structure of Continuous Word Spaces
- Maximilian Köper, Christian Scheible, Sabine Schulte im Walde
- LinguisticsInternational Conference on Computational…
- 1 April 2015
The results show that (i) morphological complexity causes a drop in accuracy, and (ii) continuous representations lack the ability to solve analogies of paradigmatic relations.
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
- Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
- Computer ScienceWASSA@EMNLP
- 1 September 2017
This paper compares several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class) and shows that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-BSTMs are particularly good at fine-grained sentiment tasks.
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
- K. Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
- Computer Science, LinguisticsConference of the European Chapter of the…
- 11 January 2017
A novel neural network model AntSynNET is presented that exploits lexico-syntactic patterns from syntactic parse trees and successfully integrates the distance between the related words along the syntactic path as a new pattern feature.
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