# Contradiction Detection with Contradiction-Specific Word Embedding
## 5 Related Work  
A brief review of related works is presented from two perspectives: contradiction detection and learning continuous representations for a specific task. **Contradiction Detection**. A strict logical definition of contradiction is that two sentences are contradictory if they cannot both be true in any world. The definition is loosened to capture human intuitions of incompatibility and better fit applications of recognizing discrepancies of the same event [1]. The looser definition of contradiction is that two sentences are contradictory when they are unlikely to be true at the same time. Contradiction detection aims to detect the semantic relation of contradiction among sentences. [PERSON] et al. first argued the importance of handling contradiction in text understanding [2]. As a kind of relation in the entailment recognition problem, contradiction has been studied continually in Recognizing Textual Entailment Challenges [13; 24; 25; 26; 27; 28; 29]. In previous years, researchers undertook the task by resolving some of the contradiction phenomena [1; 30], like negation, antonyms, data/number mismatch and different structure. [PERSON] et al. utilized functional relations to recognize whether contradictions were apparent or actual [31]. In recent years, some researchers have focussed on finding contradictory parts in a pair of sentences [32; 33; 34; 35]; however, they could not capture the contradictory relation between whole sentences. Our method captures both the global and local semantic relations. As a crucial part of contradiction analysis, antonyms detection has been gaining increasing attention from researchers [7; 36]. [PERSON] et al. used a few \"incompatibility\" patterns to acquire antonyms [37]. [PERSON] et al. expanded an antonym list for a word by adding words from the same synset in WordNet according to the direct antonym of the word [1]. VerbOcean is also used as a lexical resource. However, employing lexical resources is limited by low coverage. [PERSON] et al. presented a kind of sematic orientation, namely, excitatory or inhibitory, such as the words \"cause\" and \"ruin\". The authors argue that excitation is useful in extracting antonyms. However, the approach still cannot overcome the low coverage problem [38]. A multi-relational latent semantic analysis presented by [PERSON] et al. [39] uses a three-way tensor to combine multiple relations between two words, in which one of the relations is antonymous. The authors use continuous space representations to capture lexical semantics through tensor decomposition techniques. **Word Representation Learning**. Word representation is central to natural language processing (NLP). [PERSON] states a distributional hypothesis that words in similar contexts have the same meanings [40]. Based on the distributional hypothesis, many methods are context-based learning word representations [41; 42]. Since the development of the neural language model [43; 44; 45; 46; 47], it has become a popular approach to represent a word through a low-dimensionality continuous real-valued vector [10; 48; 49; 50; 51]. Traditional representation learning methods aim to capture semantic and syntactic similarities between two words [52]. A graph-based learning method is used for retrofitting word embedding by utilizing semantic lexicons [53]. However, contrasting relation is also a semantic relation between words. With the aim to resolve the sentiment contrast, a sentiment-specific word embedding [54] is learnt by weakly-supervised tweets collected by positive and negative emotions. Some neural network based models are proposed to revisit word embedding for lexical contrast [7; 8; 9; 10]. There are two ways