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Distinguishing antonymy, synonymy and hypernymy with distributional and distributed vector representations and neural networks

In the last decade, computational models that distinguish semantic relations have become crucial for many applications in Natural Language Processing (NLP), such as machine translation, question answering, sentiment analysis, and so on. These computational models typically distinguish semantic relations by either representing semantically related words as vector representations in the vector space, or using neural networks to classify semantic relations. In this thesis, we mainly focus on the improvement of such computational models. Specifically, the goal of this thesis is to address the tasks of distinguishing antonymy, synonymy, and hypernymy. For the task of distinguishing antonymy and synonymy, we propose two approaches. In the first approach, we focus on improving both families of word vector representations, which are distributional and distributed vector representations. Regarding the improvement of distributional vector representation, we propose a novel weighted feature for constructing word vectors by relying on distributional lexical contrast, a feature capable of differentiating between antonymy and synonymy. In terms of the improvement of distributed vector representations, we propose a neural model to learn word vectors by integrating distributional lexical contrast into the objective function of the neural model. The resulting word vectors can distinguish antonymy from synonymy and predict degrees of word similarity. In the second approach, we aim to use lexico-syntactic patterns to classify antonymy and synonymy. To do so, we propose two pattern-based neural networks to distinguish antonymy from synonymy. The lexico-syntactic patterns are induced from the syntactic parse trees and then encoded as vector representations by neural networks. As a result, the two pattern-based neural networks improve performance over prior pattern-based methods. For the tasks of distinguishing hypernymy, we propose a novel neural model to learn hierarchical embeddings for hypernymy detection and directionality. The hierarchical embeddings are learned according to two underlying aspects (i) that the similarity of hypernymy is higher than similarity of other relations, and (ii) that the distributional hierarchy is generated between hyponyms and hypernyms. The experimental results show that hierarchical embeddings significantly outperform state-of-the-art word embeddings. In order to improve word embeddings for measuring semantic similarity and relatedness, we propose two neural models to learn word denoising embeddings by filtering noise from original word embeddings without using any external resources. Two proposed neural models receive original word embeddings as inputs and learn denoising matrices to filter noise from original word embeddings. Word denoising embeddings achieve the improvement against original word embeddings over tasks of semantic similarity and relatedness. Furthermore, rather than using English, we also shift the focus on evaluating the performance of computational models to Vietnamese. To that effect, we introduce two novel datasets of (dis-)similarity and relatedness for Vietnamese. We then make use of computational models to verify the two datasets and to observe their performance in being adapted to Vietnamese. The results show that computational models exhibit similar behaviour in the two Vietnamese datasets as in the corresponding English datasets.