# A Polarity Capturing Sphere for Word to Vector Representation
## 2 Literature Review
This imposes a challenge in differentiating between antonymous word pairs and unrelated word pairs since both will have low similarity scores. [PERSON] et al. [35] proposed augmenting lexical contrast information to distributional word embeddings in order to enhance distinguishing between synonyms and antonyms. Moreover, the authors extended the skip-gram model to incorporate the lexical contrast information into the objective function. Motivated by the fact that antonyms mostly lie at close distances in the vector space, [PERSON] et al. [36] proposed a neural network model that is adapted to learn word embeddings for antonyms. These embeddings are used to carry out contradiction detection. In most of these aforementioned antonymy construction methods the problem is that they focus mostly on this task only. For example [29; 32; 34] only evaluated their vectors on antonymy detection without showing the performance of these vectors on synonyms or unrelated words. The goal in these works has mainly not been to obtain a global word embedding that works for synonyms, antonyms, and unrelated words. Word embeddings obtain the whole picture, as to the semantic relations of the words in a corpus. As such, they have many applications. For example, [PERSON] et al. [1] proposed a method to learn bilingual embeddings to perform a Chinese-English machine translation task. Moreover, word embeddings methods are applied to sentiment analysis; [PERSON] et al. [2] proposed a model that combines supervised and unsupervised techniques to learn word vectors that capture sentiment content. The goal was to be able to use these vectors in sentiment analysis tasks. The authors used an unsupervised probabilistic model of documents followed by a supervised model that maps a word vector to a predicted sentiment label using a logistic regression predictor that relies on sentiment annotated texts. [PERSON] et al. [3] developed a word embedding method by training three neural networks. The method relies on encoding sentiment information in the continuous representations of words. They evaluated their method on a benchmark Twitter sentiment classification dataset. [PERSON] et al. [4] employed word embeddings and deep learning to bridge the gap between different domains, thereby building a multi-domain sentiment model. [PERSON] et al. [5] used word2 vec to generate word vectors that learn contextual information. To perform sentiment analysis, the generated vectors were used to train machine learning algorithms in the form of classifiers. Question answering is another application of word embeddings; for example, [PERSON] et al. [6] tackled the rice FAQ (frequently asked question) question-answering system. The authors proposed methods based on word2 vec and LSTM (long-short term memory). The core of the system is question similarity computing, which is used to match users' questions and the questions in FAQ. [PERSON] et al. [7] designed a deep learning model based on word2 vec to find the best answers to the farmers' questions. Search service also exploits word embeddings; [PERSON] et al. [9] established a model based on word embeddings to improve the accuracy of data retrieval in the cloud. Spelling correction can also be done using word embeddings. [PERSON] et al. [10] proposed a method of correcting misspelled words in Twitter messages by using an improved Word2 Vec. The authors in [11] proposed crowdsourcing where the relevance between task and worker is obtained. The proposed model involves the computation of the similarity of word vectors and the establishment of the semantic tags similar matrix database based on the Word2 vec deep learning. [PERSON] et al. [12] proposed a method based on deep learning and statistical word embeddings to recognize biomedical named entities (NER), such as genes, chemicals, and diseases. Word embeddings have also tackled the field of text summarization; to achieve automatic summarization [PERSON] et al. [13] proposed the use of continuous vector representations as a basis for measuring similarity. [PERSON] et al.