Research On Automatic Answering Technique Of English Test

Artificial intelligence has developed rapidly in recent years,and the research on cognitive intelligence technology with the goal of natural language understanding has attracted extensive attention.In order to give the machine a deep understanding and reasoning ability of natural language,the key is to study how to model the language of different level,such as word,sentence,document.This thesis starts from a basic automatic sentence answering task in the field of natural language understanding,and explores the automatic sentence answering technology with high precision and flexible scalability.The task of sentence automatic answering is to select the best candidate answer from a given set of words or phrases to fill in the appropriate position of the target sentence,so that the target sentence has the correct grammar and complete semantics.At present,the representative automatic sentence answering technology includes traditional word features and language model based methods.It lacks effective use of various types of information,and the sentence answering system has a poor accuracy.At the same time,in recent years,the technology represented by deep learning has been widely used in the field of natural language understanding,but the work of effectively applying it to the sentence automatic answering task is less,and it is impossible to fuse various features,such as lexical features,syntactic features,etc.,and make reasonable integration learning under the unified deep learning framework.The scalability of existing methods is very poor.This thesis starts with the analysis of the technical research status in the field of automatic sentence answering,and proposes the research of automatic answering technology for English test.The thesis conducts research work in four aspects:multi-dimensional semantic analysis,deep semantic modeling,deep sentence embedding based on syntax,and deep semantic feature fusion.The specific description of each work is as follows:Firstly,the sentence automatic answering method based on multi-dimensional semantic analysis is studied.Traditional language models and latent semantic analysis methods,which are widely used in this field,can only model short-term word contexts,and cannot accurately model long-distance dependences and grammatical problems.Aiming at the above problems,this paper proposes a method of automatic regular collocation mining and verb tense prediction.We proposed a model that fuse multi?dimensional semantic features.Compared with the traditional method,the method significantly improves the performance of sentence automatic answering.Secondly,the method of sentence answering questions based on deep semantic representation is studied.Aiming at the problem of high-dimensional and sparse representation in traditional machine learning,this paper introduces word embedding which represent semantics in a low dimensional continuous vector space into the task of sentence automatic answering.An automatic sentence answering framework based on deep learning to rank is proposed.Furthermore,in order to alleviate the semantic inaccuracy of word embedding,this paper innovatively proposes a word embedding enhancement model for contrast meaning representation.This model gets the state of art performance on the public GRE "most opposite question" dataset.When we integrate the enhanced word embedding into a deep sentence embedding framework,it makes significant improvement in automatic sentence answering task.Thirdly,this thesis studies the deep semantic answering method based on syntactic structure information.The traditional sentence embedding methods based the existing deep learning models,such as recurrent neural network and convolutional neural network,cannot describe the sentence grammatic structure and other information.In recent years,although there are research work on how to model the syntax structure,they all show problems such as low efficiency and poor scalability.This paper proposes a sentence embedding method based on serialized syntax structure to effectively solve the problem of using syntactic information.And in the automatic sentence answering task,this approach greatly improves system performance and provides important support for subsequent fusion models.Finally,a method based on deep semantic feature fusion is studied.This method is motivated by the incapability of some semantic details in unified sentence embedding.A deep ranking model based on hidden state information around the position of answer is proposed.Combined with the various methods proposed above,a unified multi-source information fusion model is constructed.The optimal performance is obtained in the automatic sentence answering task of English test questions.