HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment
This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task{'}s organizers.
Code (0)
등록된 구현이 없습니다.
Tasks
Learning Word EmbeddingsSemantic Textual SimilarityTask 2Word EmbeddingsWord SimilaritySimilar Papers 제목 키워드 기반
HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition
This paper describes the winning system for SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. Humor detection has up until now been predominantly addressed using feature-based approaches. Our system utiliz…
Humor DetectionWord EmbeddingsTweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine
We present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, emotion-, and color-related…
Task 2Word EmbeddingsLearning to Hash with Binary Reconstructive Embeddings
Fast retrieval methods are increasingly critical for many large-scale analysis tasks, and there have been several recent methods that attempt to learn hash functions for fast and accurate nearest neighbor searches. In t…
RetrievalDataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison
In this paper we present a deep-learning system that competed at SemEval-2017 Task 6 ''{\#}HashtagWars: Learning a Sense of Humor{''}. We participated in Subtask A, in which the goal was, given two Twitter messages, to i…
Feature EngineeringHumor DetectionWord Embeddingsgundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data
The phenomenon of mixing the vocabulary and syntax of multiple languages within the same utterance is called Code-Mixing. This is more evident in multilingual societies. In this paper, we have developed a system for SemE…
Sentiment AnalysisWord Embeddings