CogALex-V Shared Task: Mach5 -- A traditional DSM approach to semantic relatedness
This contribution provides a strong baseline result for the CogALex-V shared task using a traditional {``}count{''}-type DSM (placed in rank 2 out of 7 in subtask 1 and rank 3 out of 6 in subtask 2). Parameter tuning experiments reveal some surprising effects and suggest that the use of random word pairs as negative examples may be problematic, guiding the parameter optimization in an undesirable direction.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations
The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims at providing a common benchmark for testing current corpus-based methods for the identification of lexical semantic relations (syno…
Language AcquisitionParaphrase GenerationCogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks
In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared tas…
Machine TranslationParaphrase GenerationQuestion AnsweringRelation+2CogALex-V Shared Task: ROOT18
In this paper, we describe ROOT 18, a classifier using the scores of several unsupervised distributional measures as features to discriminate between semantically related and unrelated words, and then to classify the rel…
CogALex-VI Shared Task: Bidirectional Transformer based Identification of Semantic Relations
This paper presents a bidirectional transformer based approach for recognising semantic relationships between a pair of words as proposed by CogALex VI shared task in 2020. The system presented here works by employing BE…
The CogALex Shared Task on Monolingual and Multilingual Identification of Semantic Relations
The shared task of the CogALex-VI workshop focuses on the monolingual and multilingual identification of semantic relations. We provided training and validation data for the following languages: English, German and Chine…
Relation