A word family is the base form of a word plus its inflected forms and derived forms made with suffixes and prefixes[1] plus its cognates, i.e. all words that have a common etymological origin, some of which even native speakers don't recognize as being related (e.g. "wrought (iron)" and "work(ed)").[2] In the English language, inflectional affixes include third person -s, verbal -ed and -ing, plural -s, possessive -s, comparative -er and superlative -est. Derivational affixes include -able, -er, -ish, -less, -ly, -ness, -th, -y, non-, un-, -al, -ation, -ess, -ful, -ism, -ist, -ity, -ize/-ise, -ment, in-.[1] The idea is that a base word and its inflected forms support the same core meaning, and can be considered learned words if a learner knows both the base word and the affix. Bauer and Nation proposed seven levels of affixes based on their frequency in English.[3] It has been shown that word families can assist with deriving related words via affixes, along with decreasing the time needed to derive and recognize such words.[4]
Familect
Familects often gain vocabulary through the words young children create as they learn to talk, when these words are adopted by the family.[1][2] Familects also gain vocabulary through slips of the tongue and word invention.[3]
1K views | A "familect" is a family's own private language. The word...
A "familect" is a family's own private language. The word comes from a blend of "family" and "dialect."
Online Etymology Dictionary
The online etymology dictionary (etymonline) is the internet's go-to source for quick and reliable accounts of the origin and history of English words, phrases, and idioms.
Chimbridge Singlish Dictionary
Etymology, Definitions, Meaning, Usage, Synonyms, Antonyms, Origins, Languages, and more
Every family has a secret language. Experts call it ‘familect.’
The memory and meaning in our weird, family-specific words, often born of the wild minds of children.
What is a word that is used in your family all the time, and you wish everyone else used it, too.
149 votes, 880 comments. Could be a word your family invented, or just an obscure one. UPDATE: So many great words! Anyway we can turn this into an…
Formulaic language
Formulaic language (previously known as automatic speech or embolalia) is a linguistic term for verbal expressions that are fixed in form, often non-literal in meaning, with attitudinal nuances, and closely related to communicative-pragmatic context.[1]
Cological Words
Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models
Scientific English is currently undergoing rapid change, with words like "delve," "intricate," and "underscore" appearing far more frequently than just a few years ago. It is widely assumed that scientists' use of large language models (LLMs) is responsible for such trends. We develop a formal, transferable method to characterize these linguistic changes. Application of our method yields 21 focal words whose increased occurrence in scientific abstracts is likely the result of LLM usage. We then pose "the puzzle of lexical overrepresentation": WHY are such words overused by LLMs? We fail to find evidence that lexical overrepresentation is caused by model architecture, algorithm choices, or training data. To assess whether reinforcement learning from human feedback (RLHF) contributes to the overuse of focal words, we undertake comparative model testing and conduct an exploratory online study. While the model testing is consistent with RLHF playing a role, our experimental results suggest that participants may be reacting differently to "delve" than to other focal words. With LLMs quickly becoming a driver of global language change, investigating these potential sources of lexical overrepresentation is important. We note that while insights into the workings of LLMs are within reach, a lack of transparency surrounding model development remains an obstacle to such research.
Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models
Scientific English is currently undergoing rapid change, with words like "delve," "intricate," and "underscore" appearing far more frequently than just a few years ago. It is widely assumed that scientists' use of large language models (LLMs) is responsible for such trends. We develop a formal, transferable method to characterize these linguistic changes. Application of our method yields 21 focal words whose increased occurrence in scientific abstracts is likely the result of LLM usage. We then pose "the puzzle of lexical overrepresentation": WHY are such words overused by LLMs? We fail to find evidence that lexical overrepresentation is caused by model architecture, algorithm choices, or training data. To assess whether reinforcement learning from human feedback (RLHF) contributes to the overuse of focal words, we undertake comparative model testing and conduct an exploratory online study. While the model testing is consistent with RLHF playing a role, our experimental results suggest that participants may be reacting differently to "delve" than to other focal words. With LLMs quickly becoming a driver of global language change, investigating these potential sources of lexical overrepresentation is important. We note that while insights into the workings of LLMs are within reach, a lack of transparency surrounding model development remains an obstacle to such research.
How linguistics learned to stop worrying and love the language models
Language models (LMs) can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.
Linkword
Linkword is a mnemonic system promoted by Michael Gruneberg since at least the early 1980s for learning languages based on the similarity of the sounds of words. The process involves creating an easily visualized scene that will link the words together. One example is the Russian word for cow : think and visualize "I ran my car over a cow."
Is Cross-Lingual Transfer in Bilingual Models Human-Like? A Study with Overlapping Word Forms in Dutch and English
Bilingual speakers show cross-lingual activation during reading, especially for words with shared surface form. Cognates (friends) typically lead to facilitation, whereas interlingual homographs (false friends) cause interference or no effect. We examine whether cross-lingual activation in bilingual language models mirrors these patterns. We train Dutch-English causal Transformers under four vocabulary-sharing conditions that manipulate whether (false) friends receive shared or language-specific embeddings. Using psycholinguistic stimuli from bilingual reading studies, we evaluate the models through surprisal and embedding similarity analyses. The models largely maintain language separation, and cross-lingual effects arise primarily when embeddings are shared. In these cases, both friends and false friends show facilitation relative to controls. Regression analyses reveal that these effects are mainly driven by frequency rather than consistency in form-meaning mapping. Only when just friends share embeddings are the qualitative patterns of bilinguals reproduced. Overall, bilingual language models capture some cross-linguistic activation effects. However, their alignment with human processing seems to critically depend on how lexical overlap is encoded, possibly limiting their explanatory adequacy as models of bilingual reading.
