CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes

Abstract

This article describes the unsupervised strategy submitted by the CitiusNLP team to SemEval 2018 Task 10, a task which consists of predicting whether a word is a discriminative attribute between two other words. The proposed strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70% accuracy when it is applied on the development dataset, but its accuracy goes down (63%) on the official test dataset.

Identifying and Explaining Discriminative Attributes

“…(No expl. ) 0.69 (Attia et al, 2018) Google 5 grams and Word2Vec embeddings as features for feedforward neural network None 0.67 (Zhou et al, 2018) Ensemble ML model with WordNet, PMI scores, Word2Vec, and GloVe embeddings None 0.67 (Kulmizev et al, 2018) A combination of GloVe and Paragram embeddings None 0.67 (Zhang and Carpuat, 2018) SVM with GloVe embeddings None 0.67 (Vinayan et al, 2018) CNN with GloVe embeddings None 0.66 (Grishin, 2018) Similarity calculations using a combination of DSMs None 0.65 Word2Vec, GloVe, and FastText embeddings as features for MLP-CNN None 0.63 (Gamallo, 2018) Dependency parsing and co-occurrence analysis Transp. (No expl.)…”

“…With regard to interpretability and explainability we can classify IDA approaches into three categories. Frequency-based models over textbased features, heavily relying on textual features and frequency-based methods (Gamallo, 2018;González et al, 2018) ; ML over Textual features (Dumitru et al, 2018;Sommerauer et al, 2018;King et al, 2018;Mao et al, 2018) and ML over dense vectors and textual features (Brychcín et al, 2018;Attia et al, 2018;Dumitru et al, 2018;Arroyo-Fernández et al, 2018;Speer and Lowry-Duda, 2018;Santus et al, 2018;Grishin, 2018;Zhou et al, 2018;Vinayan et al, 2018;Kulmizev et al, 2018;Zhang and Carpuat, 2018;Shiue et al, 2018). While the first category concentrates on models with higher interpretability, none of these models provide explanations.…”