Neural network techniques are widely applied to obtain high-quality distributed representations of words, i.e., word embeddings, to address text mining, information retrieval, and natural language processing tasks. Recently, efficient methods have been proposed to learn word embeddings from context that captures both semantic and syntactic relationships between words. However, it is challenging to handle unseen words or rare words with insufficient context. In this paper, inspired by the study on word recognition process in cognitive psychology, we propose to take advantage of seemingly less obvious but essentially important morphological knowledge to address these challenges. In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings. Meanwhile, the learning architecture is also able to refine the pre-defined morphological knowledge and obtain more accurate word similarity. Experiments on an analogical reasoning task and a word similarity task both demonstrate that the proposed KNET framework can greatly enhance the effectiveness of word embeddings.
There are some knowledge related word embedding works in the literature, but most of them were targeted at the problems of knowledge base completion and enhancement @cite_18 @cite_17 @cite_31 rather than producing high-quality word embeddings, which is different with our work. In contrast, some recent efforts have explored how to take advantage of knowledge to product better word embedding. For example, @cite_29 introduced a co-learning framework to produce both the word representation and the morpheme representation such that each of them can be mutually reinforced. @cite_0 proposed a new learning objective that integrates both a neural language model objective and a semantic prior knowledge objective which can result in better word embedding for semantic tasks. Moreover, a recent work @cite_36 took empirical studies on how to incorporate various types of knowledge in order to enhance word embedding. According to this work, morphological, syntactic, and semantic knowledge are all valuable to improve the quality of word embedding. In this paper, as we aim at obtaining high-quality word embeddings for rare words and unknown words, we focus on leveraging morphological knowledge since it can generate critical correlation between rare unknown words with popular ones.
@cite_18 @cite_36 @cite_29 @cite_0 @cite_31 @cite_17
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The basis of applying deep learning to solve natural language processing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually contains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it. Fortunately, text itself already contains well-defined morphological and syntactic knowledge; moreover, the large amount of texts on the Web enable the extraction of plenty of semantic knowledge. Therefore, it makes sense to design novel deep learning algorithms and systems in order to leverage the above knowledge to compute more effective word embeddings. In this paper, we conduct an empirical study on the capacity of leveraging morphological, syntactic, and semantic knowledge to achieve high-quality word embeddings. Our study explores these types of knowledge to define new basis for word representation, provide additional input information, and serve as auxiliary supervision in deep learning, respectively. Experiments on an analogical reasoning task, a word similarity task, and a word completion task have all demonstrated that knowledge-powered deep learning can enhance the effectiveness of word embedding. The techniques of using neural networks to learn distributed word representations (i.e., word embeddings) have been used to solve a variety of natural language processing tasks. The recently proposed methods, such as CBOW and Skip-gram, have demonstrated their effectiveness in learning word embeddings based on context information such that the obtained word embeddings can capture both semantic and syntactic relationships between words. However, it is quite challenging to produce high-quality word representations for rare or unknown words due to their insufficient context information. In this paper, we propose to leverage morphological knowledge to address this problem. Particularly, we introduce the morphological knowledge as both additional input representation and auxiliary supervision to the neural network framework. As a result, beyond word representations, the proposed neural network model will produce morpheme representations, which can be further employed to infer the representations of rare or unknown words based on their morphological structure. Experiments on an analogical reasoning task and several word similarity tasks have demonstrated the effectiveness of our method in producing high-quality words embeddings compared with the state-of-the-art methods. Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that incorporates both a neural language model objective (, 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements. This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over methods that rely on text features alone. Knowledge bases are an important resource for question answering and other tasks but often suffer from incompleteness and lack of ability to reason over their discrete entities and relationships. In this paper we introduce an expressive neural tensor network suitable for reasoning over relationships between two entities. Previous work represented entities as either discrete atomic units or with a single entity vector representation. We show that performance can be improved when entities are represented as an average of their constituting word vectors. This allows sharing of statistical strength between, for instance, facts involving the "Sumatran tiger" and "Bengal tiger." Lastly, we demonstrate that all models improve when these word vectors are initialized with vectors learned from unsupervised large corpora. We assess the model by considering the problem of predicting additional true relations between entities given a subset of the knowledge base. Our model outperforms previous models and can classify unseen relationships in WordNet and FreeBase with an accuracy of 86.2 and 90.0 , respectively.