Literal Translation plus Explanation in Translating Political Words

Political words are closely related to the current situations and affairs of China, which is often dependent on China's culture and history. Therefore it is not uncommon that there are some words with background knowledge known to the source language readers, but unknown to the target language readers. Literal translation plus explanation is particularly useful in translating these words with particular background and cultural connotations.

Solution to new word perplexity in immersion bilingual teaching of engineering graphics

Numerous and specialized words are main obstacles in immersion bilingual teaching of engineering graphics. A feasible solution to this problem is given by classifying new words into three categories. The fear of new words among students is overcome and the effect of bilingual teaching is greatly improved .

Improving the Collocation Extraction Method Using an Untagged Corpus for Persian Word Sense Disambiguation

Word sense disambiguation is used in many natural language processing fields. One of the ways of disambiguation is the use of decision list algorithm which is a supervised method. Supervised methods are considered as the most accurate machine learning algorithms but they are strongly influenced by knowledge acquisition bottleneck which means that their efficiency depends on the size of the tagged training set, in which their preparation is difficult, time-consuming and costly. The proposed method in this article improves the efficiency of this algorithm where there is a small tagged training set. This method uses a statistical method for collocation extraction from a big untagged corpus. Thus, the more important collocations which are the features used for creation of learning hypotheses will be identified. Weighting the features improves the efficiency and accuracy of a decision list algorithm which has been trained with a small training corpus.

Application of Word Embedding to Drug Repositioning

As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

敦煌文献异形词例释

敦煌文献词语形多俗讹、音多借字,由此形成很多同词异写的“异形词”,为今人的释录造成了相当的困难。整理这批文献时,首先必需对这些“异形词”进行“认同”,即从读音、词义的角度考证该“异形词”为某个“正字词”的不同书写形式,将这些异形词与它对应的正字词进行沟通“认同”,从而准确理解文意,提高相关文献的整理质量。本文对其中的“牛”“英拂”等十余个异形词作了较为详尽的考释。

《清代冕宁司法档案全编》俗语词考释五则

《清代冕宁司法档案全编》是记载西南少数民族地区民族纠纷通过司法审判的最早档案,极具民族性。档案中俗语词丰富,其中出现了“承缉”“逗逼”“飞覆”“越日”“争角”等俗语词,令人费解。结合辞书、传世文献及语境,“承缉”即“承担抓捕”,“逗逼”即“逗留,耽搁”,“飞覆”即“迅速回复”,“越日”即“跨过一日,次日;隔天”,“争角”即“争执、吵架”。

汉俄语言接触中俄语对汉语语义系统的影响新探

从十月革命胜利至新中国成立初期,汉俄语言之间发生了史无前例的大规模密切接触。汉俄语言接触研究对于丰富中国社会语言学语言接触理论,认识当代汉语的形成和发展具有重要意义。本文以社会语言学语言接触理论为视角,综合运用共时研究法、历时研究法和汉俄对比法,依托汉俄构词学、词汇学和语义学理论知识,从显性影响、隐性影响、组合关系影响和聚合关系影响四个方面出发,深入探讨汉俄语言接触中俄语对汉语语义系统的影响。

基于节点词全句共现的动态词义消歧研究

文章根据词义消歧即将词义回归语境这一特性,提出了一种基于节点词全句共现的动态词义消歧方法。该方法首先以全句为窗口限定节点词的使用语境,其次使用互信息(MI)、卡方检验(χ^(2)检验)和相对词序比(RRWR)等统计方法抽取节点词的语义相关词,并参照《同义词词林》构建相关词语义范畴库,最后以共现频数作为加权系数,依靠单义词语义聚类分布率对中低频共现多义词进行消歧。采用该方法对与“美丽”共现的1030个小于7义类的多义词进行消歧的测试试验中取得了85.2%的正确率。

铸牢中华民族共同体意识宣传教育常态化机制的学理阐释

铸牢中华民族共同体意识宣传教育常态化机制具有“交往交流交融”的生活性、“有形有感有效”的综合性、“共建共治共享”的系统性等典型特征。马克思主义理论的学理支撑、“三个有利于”的价值导向和应对现存挑战的客观需要分别是铸牢中华民族共同体意识宣传教育常态化机制生成的理论依据、价值依据和现实依据。铸牢中华民族共同体意识宣传教育常态化机制的构建,要以完善常态化领导机制为根本前提、以建立常态化叙事机制为中心环节、以打造常态化联动机制为必要条件、以健全常态化激励机制为内驱动力、以创新常态化评价机制为优化举措。

战国竹书释读拾遗

本文将上博简四《曹沫之阵》简56的“不皆”读为“不懈”,指不懈怠;将《曹沫之阵》的“其赏[言歲]且不中”读为“其赏阙且不中”,意为敌方的奖赏少且不公正;对清华简《说命上》简1-2的几处疑难字词进行了释读,并重新讨论了相关简文的断读和文义;认为清华简九《治政之道》简28的“其民岁猷”的“猷”可读为“就”,义为归附。

Word sense disambiguation using semantic relatedness measurement

All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to WordNet’s synsets cooperating with WordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in WordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed con- cepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD.

Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of knowledge bottleneck,i.e.,it is hard to acquire abundant disambiguation knowledge,especially in Chinese.To solve this problem,this paper proposes a graph-based Chinese WSD method with multi-knowledge integration.Particularly,a graph model combining various Chinese and English knowledge resources by word sense mapping is designed.Firstly,the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet.Then,English word similarity is computed based on English word embeddings and knowledge base.Chinese word similarity is evaluated with Chinese word embedding and HowNet,respectively.The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities,which are utilized to construct a disambiguation graph.The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words.Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.

WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS

Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.

Word sense disambiguation based on rough set

A sense feature system (SFS) is first automatically constructed from the text corpora to structurize the textural information. WSD rules are then extracted from SFS according to their certainty factors and are applied to disambiguate the senses of polysemous words. The entropy of a deterministic rough prediction is used to measure the decision quality of a rule set. Finally, the back off rule smoothing method is further designed to improve the performance of a WSD model. In the experiments, a mean rate of correction achieved during experiments for WSD in the case of rule smoothing is 0.92.

Word Sense Disambiguation in Information Retrieval

The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previous works tries to do word sense disambiguation, the process of assign a sense to a word inside a specific context, creating algorithms under a supervised or unsupervised approach, which means that those algorithms use or not an external lexical resource. This paper presents an approximated approach that combines not supervised algorithms by the use of a classifiers set, the result will be a learning algorithm based on unsupervised methods for word sense disambiguation process. It begins with an introduction to word sense disambiguation concepts and then analyzes some unsupervised algorithms in order to extract the best of them, and combines them under a supervised approach making use of some classifiers.

Chinese word sense disambiguation based on neural networks

The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to (-M,+N).The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of Mand Naffect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90.31%, and 89.62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.

Word Sense Disambiguation Model with a Cache-Like Memory Module

Word sense disambiguation(WSD),identifying the specific sense of the target word given its context,is a fundamental task in natural language processing.Recently,researchers have shown promising results using long short term memory(LSTM),which is able to better capture sequential and syntactic features of text.However,this method neglects the dependencies among instances,such as their context semantic similarities.To solve this problem,we proposed a novel WSD model by introducing a cache-like memory module to capture the semantic dependencies among instances for WSD.Extensive evaluations on standard datasets demonstrate the superiority of the proposed model over various baselines.

清代南部县衙档案俗语词考释四则

清代南部县衙档案多由人工记录,使用了“电阅”“妥僧”“硬估”“站房”等不少俗语词,这些俗语词对阅读和理解南部档案产生了障碍。根据语境和相关文献资料,认为“电阅”即“飞速明察”;“妥僧”即“做事稳当的和尚”;“硬估”即“强迫做某事,逼迫某人做某事”;“站房”即“旅馆、客栈”。

基于知识增强的文本隐喻识别图编码方法

隐喻识别是自然语言处理中语义理解的重要任务之一,目标为识别某一概念在使用时是否借用了其他概念的属性和特点.由于单纯的神经网络方法受到数据集规模和标注稀疏性问题的制约,近年来,隐喻识别研究者开始探索如何利用其他任务中的知识和粗粒度句法知识结合神经网络模型,获得更有效的特征向量进行文本序列编码和建模.然而,现有方法忽略了词义项知识和细粒度句法知识,造成了外部知识利用率低的问题,难以建模复杂语境.针对上述问题,提出一种基于知识增强的图编码方法(knowledge-enhanced graph encoding method,KEG)来进行文本中的隐喻识别.该方法分为3个部分:在文本编码层,利用词义项知识训练语义向量,与预训练模型产生的上下文向量结合,增强语义表示;在图网络层,利用细粒度句法知识构建信息图,进而计算细粒度上下文,结合图循环神经网络进行迭代式状态传递,获得表示词的节点向量和表示句子的全局向量,实现对复杂语境的高效建模;在解码层,按照序列标注架构,采用条件随机场对序列标签进行解码.实验结果表明,该方法的性能在4个国际公开数据集上均获得有效提升.