Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this en...Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in adependency tree which we call lexicalized dependency paths (LDPs). We showthat this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviatethe data sparsity problem. We apply lexicalized dependency paths to supervisedlearning using the ACE corpus and show that it can achieve similar performancelevel to other state-of-the-art methods and even surpass them on severalcategories.展开更多
Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word a...Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method.展开更多
Currently,in STEM environments,female employees are often recognized as minorities due to their positioning or occupancy rate,which may lead to experiences of“imposter syndrome”.This study applies frameworks of mixe...Currently,in STEM environments,female employees are often recognized as minorities due to their positioning or occupancy rate,which may lead to experiences of“imposter syndrome”.This study applies frameworks of mixed-gender discourse,such as limited involvement in activity as an agent,markedness,and gender-differentiated roles,to clarify how women in STEM position themselves or are positioned by the society.Using corpus linguistics and content analysis,it is clarified that female researchers are usually linguistically marked or tend to distinguish themselves as non-experts.Thus,their portrayal within a misogynistic society may considerably interact with how female researchers represent themselves.展开更多
Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown ...Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.展开更多
Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is ...Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.展开更多
Nowadays,collaborative writing has gained much attention of many scholars.And task complexity is a crucial factor that influences second language(L2)writing.However,little research has explored how task complexity aff...Nowadays,collaborative writing has gained much attention of many scholars.And task complexity is a crucial factor that influences second language(L2)writing.However,little research has explored how task complexity affects the quality of L2 collaborative writing.This study investigates the impact of task complexity on syntactic complexity,lexical complexity,and accuracy of the second language collaborative writing.English learners(N=50)in a Chinese university were required to complete two writing tasks collaboratively:a simple task and a complex task.Through analyzing their compositions,we found that task complexity has a significant impact on syntactic complexity and high complexity writing tasks help increase the syntactic complexity of second language collaborative writing.However,task complexity has little impact on lexical complexity and accuracy.The accuracy of writing tasks is largely influenced by the task requirements.The research results may enhance the understanding of collaborative writing and task complexity and provide valuable guidance for the second language teaching.展开更多
The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidit...The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidity implementations for instance for Casemix-based healthcare funding and managing systems. This higher level of granularity is worldwide spread under the umbrella of several national modifications named ICD10 XM. These developments have met the increased use of the International Clinical Reference Terminology named SNOMED. When the updating of WHO ICD10 to WHO ICD11 was decided a merging was envisaged and a WHO SNOMED CT common work proposed a methodology to create a common formal ontology between the 11th version of the WHO International Classification of Diseases and Health Problems (ICD) and the most used in the world clinical terminology named Systematized Nomenclature of Human and Veterinary Medicine - Clinical Terms (SCT). The present work follows this unachieved work and aims to develop a SNOMED-based formal ontology for ICD11 chapter 1 using the textual definitions of ICD11 codes which is a completely new character of ICD and the ontology tools provided by SCT in the publicly available SNOMED Browser. There are two key results: the lexical alignment is complete and the ontology alignment is incomplete with the validated SNOMED concept model can be completed with not yet validated attributes and values of the SNOMED Compositional Grammar. The work opens a new era for the seamless use of both international terminologies for morbidity for instance for DRG/Casemix and clinical management use. The main limitation is that it is restricted to 1 out of 26 chapters of ICD11.展开更多
As a key part of the manufacturing industry,the rubber and plastic industry is widely used in a variety of fields such as automotive manufacturing,construction materials,and electronics.With technological innovation,g...As a key part of the manufacturing industry,the rubber and plastic industry is widely used in a variety of fields such as automotive manufacturing,construction materials,and electronics.With technological innovation,global economic growth and infrastructure construction driving the demand for rubber and plastic materials,rubber and plastic materials play an important role in various fields,and accurate translation of the industry’s terminology is also crucial for international communication.The purpose of this paper is to explore in depth the lexical features of rubber and plastic industry terminology,including strong specialization,specialization of common vocabulary,and diversity of word formation.In view of these lexical features,relevant Chinese translation strategies are discussed to ensure the accuracy,specialization and comprehensibility of the translation of rubber and plastic industry terms.展开更多
Medical English,as a specialized form of English,has attracted considerable attention due to its unique linguistic features.Grammaticalization and lexicalization represent critical mechanisms in the evolution of langu...Medical English,as a specialized form of English,has attracted considerable attention due to its unique linguistic features.Grammaticalization and lexicalization represent critical mechanisms in the evolution of language and are essential for understanding and mastering medical English.This study,drawing on the MEDLINE corpus,investigates the characteristics of grammaticalization and lexicalization in medical English from the perspective of cognitive linguistics,aiming to unveil its underlying cognitive foundations.The findings suggest that the processes of grammaticalization and lexicalization in medical English reflect cognitive mechanisms such as categorization,inferential processing,and pragmatic strategies.These insights provide novel approaches and methodologies for the teaching of medical English.展开更多
This paper explores the lexical association patterns of English as a second language and their relationship with language proficiency.Through the vocabulary association test,the study analyzes the differences in vocab...This paper explores the lexical association patterns of English as a second language and their relationship with language proficiency.Through the vocabulary association test,the study analyzes the differences in vocabulary association between learners with different language levels.The participants were 100 non-native English-speaking un-dergraduate students from a top 200 university,such as the University of Nottingham,and a university outside the top 200,such as the University of Aberdeen;the two groups of learners differed in their vocabulary size and learning style.It was found that the two groups of learners differed significantly in vocabulary size,language background,and learning experience.In addition,the study raises three core questions:first,learners’lexical association patterns,second,dif-ferences in association among learners with different language proficiency levels,and third,other variables that affect vocabulary association ability.The limitations of the study are that reaction time was not measured and the influence of native language background on word association was not fully considered;future research should further explore these aspects.展开更多
[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm su...[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.展开更多
I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-...I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-word unit of varying lengths,which has a fixed orrelatively fixed structure and expresses a certain meaning.It is prefabri-cated and frequently used.As a language teacher I think chunks arevery useful in language teaching and the lexical approach is a way of improving my teaching.They make sense in the classroom as they展开更多
Because of poor vocabulary, improper use of lexis, and interference of first language, it is difficult for students in voca-tional school to learn Business English Correspondence well. Business English has close relat...Because of poor vocabulary, improper use of lexis, and interference of first language, it is difficult for students in voca-tional school to learn Business English Correspondence well. Business English has close relations with lexical chunks, whichmakes it necessary to apply lexical approach to teaching of Business English Correspondence. This paper intends to explore its the-oretical foundations and emphasize how to apply lexical approach to teaching of Business English Correspondence in vocationalschool.展开更多
The paper discusses SL learners＇ lexical competence from O＇Connor＇s Five Steps of Vocabulary Acquisition Theory, Collins ＆ Loftus＇s Semantic Network Theory and Fay ＆ Cutler＇s Mental Lexicon Theory. From the dis...The paper discusses SL learners＇ lexical competence from O＇Connor＇s Five Steps of Vocabulary Acquisition Theory, Collins ＆ Loftus＇s Semantic Network Theory and Fay ＆ Cutler＇s Mental Lexicon Theory. From the discussion, the author finds that second language learners＇ lexical competence includes the following aspects： the ability to recall the words which are similar to the target words in spelling and pronunciation; the ability to recall the synonyms of the target words; the ability to recall the antonyms of the target words; and the ability to recall the related words which come together with the target words in a context. Meanwhile it advances some strategies to develop the corresponding competence.展开更多
The essay briefly illustrates E-C and C-E Translation strategies in the light of lexical similarity and differences of Lexical Contrastive Analysis, and explores lexical translation skills under the theory of Cultural...The essay briefly illustrates E-C and C-E Translation strategies in the light of lexical similarity and differences of Lexical Contrastive Analysis, and explores lexical translation skills under the theory of Cultural Differences.展开更多
Ⅰ. Each of the Statements below is followed by four alternative answers. Choose the one that would best complete the statement and put the letter in the bracket.1. Jack of all trades is an idiom_.A. nominal in nature...Ⅰ. Each of the Statements below is followed by four alternative answers. Choose the one that would best complete the statement and put the letter in the bracket.1. Jack of all trades is an idiom_.A. nominal in nature B. adjectival in nature C. verbal in nature D. adverbial in nature2. Let the dog see the rabbit is an idiom_.A. nominal in nature B. adjectival in展开更多
文摘Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in adependency tree which we call lexicalized dependency paths (LDPs). We showthat this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviatethe data sparsity problem. We apply lexicalized dependency paths to supervisedlearning using the ACE corpus and show that it can achieve similar performancelevel to other state-of-the-art methods and even surpass them on severalcategories.
基金supported by the National Natural Science Foundation of China（No.61303082） the Research Fund for the Doctoral Program of Higher Education of China（No.20120121120046）
文摘Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method.
文摘Currently,in STEM environments,female employees are often recognized as minorities due to their positioning or occupancy rate,which may lead to experiences of“imposter syndrome”.This study applies frameworks of mixed-gender discourse,such as limited involvement in activity as an agent,markedness,and gender-differentiated roles,to clarify how women in STEM position themselves or are positioned by the society.Using corpus linguistics and content analysis,it is clarified that female researchers are usually linguistically marked or tend to distinguish themselves as non-experts.Thus,their portrayal within a misogynistic society may considerably interact with how female researchers represent themselves.
基金supported by National Natural Science Foundation of China(Grant No.62266028,62266027,U21B2027,and U24A20334)Major Science and Technology Programs in Yunnan Province(Grant No.202302AD080003,202402AG050007,and 202303AP140008)+1 种基金Yunnan Province Basic Research Program(Grant No.202301AS070047,202301AT070471,and 202401BC070021)Kunming University of Science and Technology's"Double First-rate"construction joint project(Grant No.202201BE070001-021).
文摘Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.
基金supported by the International Research Center of Big Data for Sustainable Development Goals under Grant No.CBAS2022GSP05the Open Fund of State Key Laboratory of Remote Sensing Science under Grant No.6142A01210404the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant No.KLIGIP-2022-B03.
文摘Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.
文摘Nowadays,collaborative writing has gained much attention of many scholars.And task complexity is a crucial factor that influences second language(L2)writing.However,little research has explored how task complexity affects the quality of L2 collaborative writing.This study investigates the impact of task complexity on syntactic complexity,lexical complexity,and accuracy of the second language collaborative writing.English learners(N=50)in a Chinese university were required to complete two writing tasks collaboratively:a simple task and a complex task.Through analyzing their compositions,we found that task complexity has a significant impact on syntactic complexity and high complexity writing tasks help increase the syntactic complexity of second language collaborative writing.However,task complexity has little impact on lexical complexity and accuracy.The accuracy of writing tasks is largely influenced by the task requirements.The research results may enhance the understanding of collaborative writing and task complexity and provide valuable guidance for the second language teaching.
文摘The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidity implementations for instance for Casemix-based healthcare funding and managing systems. This higher level of granularity is worldwide spread under the umbrella of several national modifications named ICD10 XM. These developments have met the increased use of the International Clinical Reference Terminology named SNOMED. When the updating of WHO ICD10 to WHO ICD11 was decided a merging was envisaged and a WHO SNOMED CT common work proposed a methodology to create a common formal ontology between the 11th version of the WHO International Classification of Diseases and Health Problems (ICD) and the most used in the world clinical terminology named Systematized Nomenclature of Human and Veterinary Medicine - Clinical Terms (SCT). The present work follows this unachieved work and aims to develop a SNOMED-based formal ontology for ICD11 chapter 1 using the textual definitions of ICD11 codes which is a completely new character of ICD and the ontology tools provided by SCT in the publicly available SNOMED Browser. There are two key results: the lexical alignment is complete and the ontology alignment is incomplete with the validated SNOMED concept model can be completed with not yet validated attributes and values of the SNOMED Compositional Grammar. The work opens a new era for the seamless use of both international terminologies for morbidity for instance for DRG/Casemix and clinical management use. The main limitation is that it is restricted to 1 out of 26 chapters of ICD11.
文摘As a key part of the manufacturing industry,the rubber and plastic industry is widely used in a variety of fields such as automotive manufacturing,construction materials,and electronics.With technological innovation,global economic growth and infrastructure construction driving the demand for rubber and plastic materials,rubber and plastic materials play an important role in various fields,and accurate translation of the industry’s terminology is also crucial for international communication.The purpose of this paper is to explore in depth the lexical features of rubber and plastic industry terminology,including strong specialization,specialization of common vocabulary,and diversity of word formation.In view of these lexical features,relevant Chinese translation strategies are discussed to ensure the accuracy,specialization and comprehensibility of the translation of rubber and plastic industry terms.
基金Project of Social Science Achievement Evaluation Committee of Hunan Province of China(XSP2023WXC037)Project of Teaching Reform Research of Hunan Province of China(202401001822)。
文摘Medical English,as a specialized form of English,has attracted considerable attention due to its unique linguistic features.Grammaticalization and lexicalization represent critical mechanisms in the evolution of language and are essential for understanding and mastering medical English.This study,drawing on the MEDLINE corpus,investigates the characteristics of grammaticalization and lexicalization in medical English from the perspective of cognitive linguistics,aiming to unveil its underlying cognitive foundations.The findings suggest that the processes of grammaticalization and lexicalization in medical English reflect cognitive mechanisms such as categorization,inferential processing,and pragmatic strategies.These insights provide novel approaches and methodologies for the teaching of medical English.
文摘This paper explores the lexical association patterns of English as a second language and their relationship with language proficiency.Through the vocabulary association test,the study analyzes the differences in vocabulary association between learners with different language levels.The participants were 100 non-native English-speaking un-dergraduate students from a top 200 university,such as the University of Nottingham,and a university outside the top 200,such as the University of Aberdeen;the two groups of learners differed in their vocabulary size and learning style.It was found that the two groups of learners differed significantly in vocabulary size,language background,and learning experience.In addition,the study raises three core questions:first,learners’lexical association patterns,second,dif-ferences in association among learners with different language proficiency levels,and third,other variables that affect vocabulary association ability.The limitations of the study are that reaction time was not measured and the influence of native language background on word association was not fully considered;future research should further explore these aspects.
基金Supported by the Science Foundation of Hengyang Normal University of China(09A36)~~
文摘[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.
文摘I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-word unit of varying lengths,which has a fixed orrelatively fixed structure and expresses a certain meaning.It is prefabri-cated and frequently used.As a language teacher I think chunks arevery useful in language teaching and the lexical approach is a way of improving my teaching.They make sense in the classroom as they
文摘Because of poor vocabulary, improper use of lexis, and interference of first language, it is difficult for students in voca-tional school to learn Business English Correspondence well. Business English has close relations with lexical chunks, whichmakes it necessary to apply lexical approach to teaching of Business English Correspondence. This paper intends to explore its the-oretical foundations and emphasize how to apply lexical approach to teaching of Business English Correspondence in vocationalschool.
文摘The paper discusses SL learners＇ lexical competence from O＇Connor＇s Five Steps of Vocabulary Acquisition Theory, Collins ＆ Loftus＇s Semantic Network Theory and Fay ＆ Cutler＇s Mental Lexicon Theory. From the discussion, the author finds that second language learners＇ lexical competence includes the following aspects： the ability to recall the words which are similar to the target words in spelling and pronunciation; the ability to recall the synonyms of the target words; the ability to recall the antonyms of the target words; and the ability to recall the related words which come together with the target words in a context. Meanwhile it advances some strategies to develop the corresponding competence.
文摘The essay briefly illustrates E-C and C-E Translation strategies in the light of lexical similarity and differences of Lexical Contrastive Analysis, and explores lexical translation skills under the theory of Cultural Differences.
文摘Ⅰ. Each of the Statements below is followed by four alternative answers. Choose the one that would best complete the statement and put the letter in the bracket.1. Jack of all trades is an idiom_.A. nominal in nature B. adjectival in nature C. verbal in nature D. adverbial in nature2. Let the dog see the rabbit is an idiom_.A. nominal in nature B. adjectival in
