Neologisms in Modern English: study of word-formation processes

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On past participle agreement in transitive clauses in French

This paper provides a Minimalist analysis of past participle agreement in French in transitive clauses. Our account posits that the head v of vP in such structures carries an (accusativeassigning) structural case feature which may apply (with or without concomitant agreement) to case-mark a clause-mate object, the subject of a defective complement clause, or an intermediate copy of a preposed subject in spec-CP. In structures where a goal is extracted from vP (e.g. via wh-movement) v also carries an edge feature, and may also carry a specificity feature and a set of (number and gender) agreement features. We show how these assumptions account for agreement of a participle with a preposed specific clausemate object or defective-clause subject, and for the absence of agreement with an embedded object, with the complement of an impersonal verb, and with the subject of an embedded (finite or nonfinite) CP complement. We also argue that the absence of agreement marking (in expected contexts) on the participles faitmade and laissélet in infinitive structures is essentially viral in nature. Finally, we claim that obligatory participle agreement with reflexive and reciprocal objects arises because the derivation of reflexives involves A-movement and concomitant agreement

Product naming in the classroom: a case study of word formation

　This paper presents data collected from students from a product naming project where groups were responsible for naming four novel products. The product names were then analyzed and compared to a set of standard values for how new words typically enter the English language. This paper also discusses briefly how new words enter a language and the word formation process as it applies to the English language. Finally, implications of the results are discussed and consideration is given to how students could benefit by such product naming projects and having explicit knowledge about the word formation process in English

English compound and non-compound processing in bilingual and multilingual speakers: effects of dominance and sequential multilingualism

This article reports on a study investigating the relative influence of the first and dominant language on L2 and L3 morpho-lexical processing. A lexical decision task compared the responses to English NV-er compounds (e.g., taxi driver) and non-compounds provided by a group of native speakers and three groups of learners at various levels of English proficiency: L1 Spanish-L2 English sequential bilinguals and two groups of early Spanish-Basque bilinguals with English as their L3. Crucially, the two trilingual groups differed in their first and dominant language (i.e., L1 Spanish-L2 Basque vs. L1 Basque-L2 Spanish). Our materials exploit an (a)symmetry between these languages: while Basque and English pattern together in the basic structure of (productive) NV-er compounds, Spanish presents a construction that differs in directionality as well as inflection of the verbal element (V[3SG] + N). Results show between and within group differences in accuracy and response times that may be ascribable to two factors besides proficiency: the number of languages spoken by a given participant and their dominant language. An examination of response bias reveals an influence of the participants' first and dominant language on the processing of NV-er compounds. Our data suggest that morphological information in the nonnative lexicon may extend beyond morphemic structure and that, similarly to bilingualism, there are costs to sequential multilingualism in lexical retrieval

Patterns and Meanings of English Words Through Word Formation Processes of Acronyms, Clipping, Compound and Blending Found in Internet-Based Media

This research aims to explore the word-formation process in English new words found in the internet-based media through acronym, compound, clipping and blending and their meanings. This study applies Plag\u27s (2002) framework of acronym and compound; Jamet\u27s (2009) framework of clipping, and Algeo\u27s framework (1977) in Hosseinzadeh (2014) for blending. Despite the formula established in each respective framework, there could be occurrences of novelty and modification on how words are formed and how meaning developed in the newly formed words. The research shows that well accepted acronyms can become real words by taking lower case and affixation. Some acronyms initialized non-lexical words, used non initial letters, and used letters and numbers that pronounced the same with the words they represent. Compounding also includes numbers as the element member of the compound. The nominal nouns are likely to have metaphorical and idiomatic meanings. Some compounds evolve to new and more specific meaning. The study also finds that back-clipping is the most dominant clipping. In blending, the sub-category clipping of blending, the study finds out that when clipping takes place, the non-head element is back-clipped and the head is fore-clipped

A Corpus-based Language Network Analysis of Near-synonyms in a Specialized Corpus

As the international medium of communication for seafarers throughout the world, the importance of English has long been recognized in the maritime industry. Many studies have been conducted on Maritime English teaching and learning, nevertheless, although there are many near-synonyms existing in the language, few studies have been conducted on near-synonyms used in the maritime industry. The objective of this study is to answer the following three questions. First, what are the differences and similarities between different near-synonyms in English? Second, can collocation network analysis provide a new perspective to explain the distinctions of near-synonyms from a micro-scopic level? Third, is semantic domain network analysis useful to distinguish one near-synonym from the other at the macro-scopic level? In pursuit of these research questions, I first illustrated how the idea of incorporating collocates in corpus linguistics, Maritime English, near-synonyms, semantic domains and language network was studied. Then important concepts such as Maritime English, English for Specific Purposes, corpus linguistics, synonymy, collocation, semantic domains and language network analysis were introduced. Third, I compiled a 2.5 million word specialized Maritime English Corpus and proposed a new method of tagging English multi-word compounds, discussing the comparison of with and without multi-word compounds with regard to tokens, types, STTR and mean word length. Fourth, I examined collocates of five groups of near-synonyms, i.e., ship vs. vessel, maritime vs. marine, ocean vs. sea, safety vs. security, and harbor vs. port, drawing data through WordSmith 6.0, tagging semantic domains in Wmatrix 3.0, and conducting network analyses using NetMiner 4.0. In the final stage, from the results and discussions, I was able to answer the research questions. First, maritime near-synonyms generally show clear preference to specific collocates. Due to the specialty of Maritime English, general definitions are not helpful for the distinction between near-synonyms, therefore a new perspective is needed to view the behaviors of maritime words. Second, as a special visualization method, collocation network analysis can provide learners with a direct vision of the relationships between words. Compared with traditional collocation tables, learners are able to more quickly identify the collocates and find the relationship between several node words. In addition, it is much easier for learners to find the collocates exclusive to a specific word, thereby helping them to understand the meaning specific to that word. Third, if the collocation network shows learners relationships of words, the semantic domain network is able to offer guidance cognitively: when a person has a specific word, how he can process it in his mind and therefore find the more appropriate synonym to collocate with. Main semantic domain network analysis shows us the exclusive domains to a certain near-synonym, and therefore defines the concepts exclusive to that near-synonym: furthermore, main semantic domain network analysis and sub-semantic domain network analysis together are able to tell us how near-synonyms show preference or tendency for one synonym rather than another, even when they have shared semantic domains. The options in identifying relationships of near-synonyms can be presented through the classic metaphor of "the forest and the trees." Generally speaking, we see only the vein of a tree leaf through the traditional way of sentence-level analysis. We see the full leaf through collocation network analysis. We see the tree, even the whole forest, through semantic domain network analysis.Contents Chapter 1. Introduction 1 1.1 Focus of Inquiry 1 1.2 Outline of the Thesis 5 Chapter 2. Literature Review 8 2.1 A Brief Synopsis 8 2.2 Maritime English as an English for Specific Purposes (ESP) 9 2.2.1 What is ESP? 9 2.2.2 Maritime English as ESP 10 2.2.3 ESP and Corpus Linguistics 11 2.3 Synonymy 12 2.3.1 Definition of Synonymy 13 2.3.2 Synonymy as a Matter of Degree 15 2.3.3 Criteria for Synonymy Differentiation 18 2.3.4 Near-synonyms in Corpus Linguistics 19 2.4 Collocation 21 2.4.1 Definition of Collocation 21 2.4.2 Collocation in Corpus Linguistics 22 2.4.2.1 Definition of Collocation in Corpus Linguistics 23 2.4.2.2 Collocation vs. Colligation 24 2.4.3 Lexical Priming of Collocation in Psychology 25 2.5 Language Network Analysis 26 2.5.1 Definition 26 2.5.2 Classification 27 2.5.3 Basic Concepts 31 2.5.4 Previous Studies 33 2.6 Semantic Domain Analysis 39 2.6.1 Concepts of Semantic Domains 39 2.6.2 Previous Studies on Semantic Domain Analysis 39 Chapter 3. Data and Methodology 41 3.1 Maritime English Corpus 41 3.1.1 What is a Corpus? 41 3.1.2 Characteristics of a Corpus 42 3.1.2.1 Corpus-driven vs. Corpus-based research 42 3.1.2.2 Specialized Corpora for Specialized Discourse 43 3.1.3 Maritime English Corpus (MEC) 44 3.1.3.1 Sampling of the MEC 45 3.1.3.2 Size, Balance, and Representativeness 51 3.1.3.3 Multi-word Compounds in the MEC 53 3.1.3.4 Basic Information of the MEC 56 3.2 Methodology for Collocates Extraction 60 3.3 Methodology for Networks Visualization 63 3.4 Methodology for Semantic Tagging 65 3.5 Process of Data Analysis 69 Chapter 4. Collocation Network Analysis of Near-synonyms 70 4.1 Meaning Differences 71 4.1.1 Ship vs. Vessel 71 4.1.2 Maritime vs. Marine 72 4.1.3 Sea vs. Ocean 73 4.1.4 Safety vs. Security 74 4.1.5 Port vs. Harbor 76 4.2 Similarity Degree of Groups of Near-synonyms 76 4.2.1 Similarity Degree Based on Number of Shared Collocates 77 4.2.2 Similarity Degree Based on MI3 Cosine Similarity 78 4.3 Collocation Network Analysis 80 4.3.1 Ship vs. Vessel 80 4.3.2 Maritime vs. Marine 82 4.3.3 Sea vs. Ocean 84 4.3.4 Safety vs. Security 85 4.3.5 Port vs. Harbor 87 4.4 Advantages and Limitations of Collocation Network Analysis 88 Chapter 5. Semantic Domain Network Analysis of Near-synonyms 89 5.1 Comparison between Collocation and Semantic Domain Analysis 89 5.2 Semantic Domain Network Analysis of Exclusiveness 92 5.2.1 Ship vs. Vessel 93 5.2.2 Maritime vs. Marine 96 5.2.3 Sea vs. Ocean 99 5.2.4 Safety vs. Security 102 5.2.5 Port vs. Harbor 105 5.3 Analysis of Shared Semantic Domains 108 5.4 Advantages and Limitations of Semantic Domain Network Analysis 112 Chapter 6. Conclusion 113 6.1 Summary 113 6.2 Limitations and Implications 116 References 118 Appendix: Collocates of Near-synonyms 136Docto

Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English

Twitter constitutes a rich resource for investigating language contact phenomena. In this paper, we report findings from the analysis of a large-scale diachronic corpus of over one million tweets, containing loanwords from te reo Maori, the indigenous language spoken in New Zealand, into (primarily, New Zealand) English. Our analysis focuses on hashtags comprising mixed-language resources (which we term hybrid hashtags), bringing together descriptive linguistic tools (investigating length, word class, and semantic domains of the hashtags) and quantitative methods (Random Forests and regression analysis). Our work has implications for language change and the study of loanwords (we argue that hybrid hashtags can be linked to loanword entrenchment), and for the study of language on social media (we challenge proposals of hashtags as “words,” and show that hashtags have a dual discourse role: a micro-function within the immediate linguistic context in which they occur and a macro-function within the tweet as a whole)