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\"Spanning more than three centuries, Take My Word for It is a fascinating, one-of-a-kind window into the surprisingly short history of idioms in English. Widely known for his studies of word origins, Anatoly Liberman explains more than one thousand idioms, both popular and obscure, occurring in both American and British standard English and including many regional expressions\"-- Provided by publisher.

With nearly 2400 entries, this dictionary covers every aspect of the subject, from the most venerable work to the exciting advances of the last few years, many of which have not even made it into textbooks yet.

With nearly 2400 entries, this dictionary covers every aspect of the subject, from the most venerable work to the exciting advances of the last few years, many of which have not even made it into textbooks yet.

This is an encyclopedic dictionary of close to 400 important philosophical, literary, and political terms and concepts that defy easy--or any--translation from one language and culture to another. Drawn from more than a dozen languages, terms such asDasein(German),pravda(Russian),saudade(Portuguese), andstato(Italian) are thoroughly examined in all their cross-linguistic and cross-cultural complexities. Spanning the classical, medieval, early modern, modern, and contemporary periods, these are terms that influence thinking across the humanities. The entries, written by more than 150 distinguished scholars, describe the origins and meanings of each term, the history and context of its usage, its translations into other languages, and its use in notable texts. The dictionary also includes essays on the special characteristics of particular languages--English, French, German, Greek, Italian, Portuguese, Russian, and Spanish. Originally published in French, this one-of-a-kind reference work is now available in English for the first time, with new contributions from Judith Butler, Daniel Heller-Roazen, Ben Kafka, Kevin McLaughlin, Kenneth Reinhard, Stella Sandford, Gayatri Chakravorty Spivak, Jane Tylus, Anthony Vidler, Susan Wolfson, Robert J. C. Young, and many more.The result is an invaluable reference for students, scholars, and general readers interested in the multilingual lives of some of our most influential words and ideas. Covers close to 400 important philosophical, literary, and political terms that defy easy translation between languages and culturesIncludes terms from more than a dozen languagesEntries written by more than 150 distinguished thinkersAvailable in English for the first time, with new contributions by Judith Butler, Daniel Heller-Roazen, Ben Kafka, Kevin McLaughlin, Kenneth Reinhard, Stella Sandford, Gayatri Chakravorty Spivak, Jane Tylus, Anthony Vidler, Susan Wolfson, Robert J. C. Young, and many moreContains extensive cross-references and bibliographiesAn invaluable resource for students and scholars across the humanities

Sentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its colloquialism and multilingualism, make it different from other languages and pose additional challenges to sentiment analysis. Recent advances in models such as ChatGPT offer potential viable solutions. This study investigated the efficacy of GPT-3.5 and GPT-4 in Cantonese sentiment analysis in the context of web-based counseling and compared their performance with other mainstream methods, including lexicon-based methods and machine learning approaches. We analyzed transcripts from a web-based, text-based counseling service in Hong Kong, including a total of 131 individual counseling sessions and 6169 messages between counselors and help-seekers. First, a codebook was developed for human annotation. A simple prompt (\"Is the sentiment of this Cantonese text positive, neutral, or negative? Respond with the sentiment label only.\") was then given to GPT-3.5 and GPT-4 to label each message's sentiment. GPT-3.5 and GPT-4's performance was compared with a lexicon-based method and 3 state-of-the-art models, including linear regression, support vector machines, and long short-term memory neural networks. Our findings revealed ChatGPT's remarkable accuracy in sentiment classification, with GPT-3.5 and GPT-4, respectively, achieving 92.1% (5682/6169) and 95.3% (5880/6169) accuracy in identifying positive, neutral, and negative sentiment, thereby outperforming the traditional lexicon-based method, which had an accuracy of 37.2% (2295/6169), and the 3 machine learning models, which had accuracies ranging from 66% (4072/6169) to 70.9% (4374/6169). Among many text analysis techniques, ChatGPT demonstrates superior accuracy and emerges as a promising tool for Cantonese sentiment analysis. This study also highlights ChatGPT's applicability in real-world scenarios, such as monitoring the quality of text-based counseling services and detecting message-level sentiments in vivo. The insights derived from this study pave the way for further exploration into the capabilities of ChatGPT in the context of underresourced languages and specialized domains like psychotherapy and natural language processing.

Attitudes toward abortion have historically been characterized via dichotomized labels, yet research suggests that these labels do not appropriately encapsulate beliefs on abortion. Rather, contexts, circumstances, and lived experiences often shape views on abortion into more nuanced and complex perspectives. Qualitative data have also been shown to underpin belief systems regarding abortion. Social media, as a form of qualitative data, could reveal how attitudes toward abortion are communicated publicly in web-based spaces. Furthermore, in some cases, social media can also be leveraged to seek health information. This study applies natural language processing and social media mining to analyze Reddit (Reddit, Inc) forums specific to abortion, including r/Abortion (the largest subreddit about abortion) and r/AbortionDebate (a subreddit designed to discuss and debate worldviews on abortion). Our analytical pipeline intends to identify potential themes within the data and the affect from each post. We applied a neural network-based topic modeling pipeline (BERTopic) to uncover themes in the r/Abortion (n=2151) and r/AbortionDebate (n=2815) subreddits. After deriving the optimal number of topics per subreddit using an iterative coherence score calculation, we performed a sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner to assess positive, neutral, and negative affect and an emotion analysis using the Text2Emotion lexicon to identify potential emotionality per post. Differences in affect and emotion by subreddit were compared. The iterative coherence score calculation revealed 10 topics for both r/Abortion (coherence=0.42) and r/AbortionDebate (coherence=0.35). Topics in the r/Abortion subreddit primarily centered on information sharing or offering a source of social support; in contrast, topics in the r/AbortionDebate subreddit centered on contextualizing shifting or evolving views on abortion across various ethical, moral, and legal domains. The average compound Valence Aware Dictionary and Sentiment Reasoner scores for the r/Abortion and r/AbortionDebate subreddits were 0.01 (SD 0.44) and -0.06 (SD 0.41), respectively. Emotionality scores were consistent across the r/Abortion and r/AbortionDebate subreddits; however, r/Abortion had a marginally higher average fear score of 0.36 (SD 0.39). Our findings suggest that people posting on abortion forums on Reddit are willing to share their beliefs, which manifested in diverse ways, such as sharing abortion stories including how their worldview changed, which critiques the value of dichotomized abortion identity labels, and information seeking. Notably, the style of discourse varied significantly by subreddit. r/Abortion was principally leveraged as an information and outreach source; r/AbortionDebate largely centered on debating across various legal, ethical, and moral abortion domains. Collectively, our findings suggest that abortion remains an opaque yet politically charged issue for people and that social media can be leveraged to understand views and circumstances surrounding abortion.

This study compared an AI chatbot (Kimi) and a bilingual dictionary app (NCD) in supporting vocabulary tasks among Chinese junior English majors. Sixty-six participants used either Kimi or NCD to complete both receptive and productive lexical tasks. Questionnaires gathered user feedback on tool use, and a surprise retention test assessed long-term vocabulary retention one week later. Results showed that Kimi significantly outperformed NCD in vocabulary comprehension, collocation production, and productive knowledge retention. Additionally, Kimi demonstrated more consistent performance than NCD across all test items, highlighting its reliability. The study underscores the potential of AI chatbots to address language-related queries and enhance vocabulary acquisition. It also advocates for aligning technological advancements with pedagogical goals to optimize language learning tools and create a sustainable learning environment.

This ethnographic dictionary is the result of Hans Fischer's long-term fieldwork among the Wampar, who occupy the middle Markham Valley in Morobe Province, Papua New Guinea (PNG). Their language, Dzob Wampar, belongs to the Markham family of the Austronesian languages. Today most Wampar speak not only Wampar but also PNG's lingua franca, Tok Pisin. Six decades of Wampar research has documented the extent and speed of change in the region. Today, mining, migration and the commodification of land are accelerating the pace of change in Wampar communities, resulting in great individual differences in knowledge of the vernacular. This dictionary covers largely forgotten Wampar expressions as well as loanwords from German and Jabêm that have become part of everyday language. Most entries contain example sentences from original Wampar texts. The dictionary is complemented by an overview of ethnographic research among Wampar, a sketch of Wampar grammar, a bibliography and an English-to-Wampar finder list.

This mixed-methods study investigated the effects of different dictionary formats within Google Classroom on the translation performance and attitudes of English as a Foreign Language (EFL) learners. One hundred twenty male EFL university students in Egypt were assigned to one of three groups: an online dictionary, a CD-ROM dictionary, or a traditional paper dictionary control. Quantitative analysis revealed that both digital dictionary groups significantly outperformed the paper-based control group in translation accuracy, confidence, and enjoyment. However, while no significant quantitative differences emerged between the two digital groups, qualitative findings revealed a stark experiential divide. Participants lauded the online dictionary for its comprehensive features and empowering user experience, which fostered confidence and enjoyment, while describing the CD-ROM as limited and frustrating. These findings demonstrate that while any digital integration surpasses traditional methods, the specific pedagogical affordances of a tool are paramount; statistical parity in performance can mask crucial differences in learner agency and motivation. This study concludes that in technology-enhanced translation pedagogy, selecting tools with superior usability and rich features is essential not only for improving performance but for cultivating the positive affective experiences that underpin sustainable learning.

Purpose: Acoustic measurement of speech sounds requires first segmenting the speech signal into relevant units (words, phones, etc.). Manual segmentation is cumbersome and time consuming. Forced-alignment algorithms automate this process by aligning a transcript and a speech sample. We compared the phoneme-level alignment performance of five available forced-alignment algorithms on a corpus of child speech. Our goal was to document aligner performance for child speech researchers. Method: The child speech sample included 42 children between 3 and 6 years of age. The corpus was force-aligned using the Montreal Forced Aligner with and without speaker adaptive training, triphone alignment from the Kaldi speech recognition engine, the Prosodylab-Aligner, and the Penn Phonetics Lab Forced Aligner. The sample was also manually aligned to create gold-standard alignments. We evaluated alignment algorithms in terms of accuracy (whether the interval covers the midpoint of the manual alignment) and difference in phone-onset times between the automatic and manual intervals. Results: The Montreal Forced Aligner with speaker adaptive training showed the highest accuracy and smallest timing differences. Vowels were consistently the most accurately aligned class of sounds across all the aligners, and alignment accuracy increased with age for fricative sounds across the aligners too. Conclusion: The best-performing aligner fell just short of human-level reliability for forced alignment. Researchers can use forced alignment with child speech for certain classes of sounds (vowels, fricatives for older children), especially as part of a semi-automated workflow where alignments are later inspected for gross errors.