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The semantic representation of words is a fundamental task in natural language processing and text mining. Learning word embedding has shown its power on various tasks. Most studies are aimed at generating embedding representation of a word based on encoding its context information. However, many latent relations, such as co-occurring associative patterns and semantic conceptual relations, are not well considered. In this paper, we propose an extensible model to incorporate these kinds of valuable latent relations to increase the semantic relatedness of word pairs by learning word embeddings. To assess the effectiveness of our model, we conduct experiments on both information retrieval and text classification tasks. The results indicate the effectiveness of our model as well as its flexibility on different tasks.

Generating the precise semantic representation of a word or concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually 1) model a word's semantics from some explicit aspects while ignoring the intrinsic aspects of the word, 2) treat semantic knowledge as a supplement of word embeddings, and 3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between concept co-occurrences and hyperlinks. In human consciousness, a concept is always associated with various couplings that exist within/between descriptive texts and knowledge networks, which inspires us to capture as many concept couplings as possible for building a more informative concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings that are based on concept co-occurrences and hyperlink relations, respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings for better concept representation. Extensive experimental results on six real-world datasets show that CCE significantly outperforms eight state-of-the-art word embeddings and semantic representation methods. [ABSTRACT FROM AUTHOR]

Sentence auto-completion is an important feature that saves users many keystrokes in typing the entire sentence by providing suggestions as they type. Despite its value, the existing sentence auto-completion methods, such as query completion models, can hardly be applied to solving the object completion problem in sentences with the form of (subject, verb, object), due to the complex natural language description and the data deficiency problem. Towards this goal, we treat an SVO sentence as a three-element triple (subject, sentence pattern, object), and cast the sentence object completion problem as an element inference problem. These elements in all triples are encoded into a unified low-dimensional embedding space by our proposed TRANSFER model, which leverages the external knowledge base to strengthen the representation learning performance. With such representations, we can provide reliable candidates for the desired missing element by a linear model. Extensive experiments on a real-world dataset have well-validated our model. Meanwhile, we have successfully applied our proposed model to factoid question answering systems for answer candidate selection, which further demonstrates the applicability of the TRANSFER model. [ABSTRACT FROM PUBLISHER]