Phrase Patterns Research Articles

Optionality and paradigmaticity

Abstract Zooming in from the grand-scale descriptions of the Mainland Southeast Asian convergence area, this paper focuses on Htanaw, an Austroasiatic language in Myanmar’s Shan State. Close contact with Burmese/Intha, Pa’O, and Shan led to a restructuring of Htanaw, exemplified here by the verb phrase, which exhibits an intriguing mixture of (isolating) Austroasiatic and (morphologically complex) Tibeto-Burman features. The Htanaw VP is internally complex with optional bound morphemes expressing grammatical and semantic categories. Based on original fieldwork data and available published material, this study presents the dynamics of the Htanaw verb phrase patterns and puts them in an areal typological perspective.

Naming and Branding of Madurese Traditional Culinary

Madura has diverse culinary specialties. This aspect of culinary naming and branding is interesting to study in relation to culinary tourism. This study aims to explain aspects of food naming and their patterns in traditional Sumenep culinary delights. In addition, it also explains the branding aspects of the food. Data on food names were obtained from the official tourism guidebook issued by the Office of Culture, Youth, Sports, and Tourism of Sumenep Regency. The results of this research show that aspects of food naming are derived from the food ingredients used, the uniqueness of the food, and the culinary origin, emphasizing its origin from Madura. The food naming uses patterns of words and phrases. If it is used a single word then the name of the food is unique, and if it is used a phrase pattern then the second word explains the first word, either for material or origin. Meanwhile, the culinary branding aspect in the tourist guidebook is to use explanations of authentic, distinctive, easy to find, delicious, affordable prices and cultural heritage. The terms used provide a positive image for traditional Madurese culinary delights.

Beyond Hair Loss: Exploring the Evolution of Androgenetic Alopecia Research based on text mining and Bibliometrics

In this study, word dynamics, co-occurring phrases, and keyword frequency in the area of androgenetic alopecia were thoroughly analysed during a ten-year period. The study tracks changes in word usage, frequency, and context and identifies variations in the distribution and usage patterns of keywords, subjects, and co-occurring phrases using natural language processing techniques and graph theory-based approaches. The research reveals hidden linkages and patterns between diverse ideas, shedding light on the co-occurrence patterns of numerous phrases in the literature on androgenetic alopecia. The study emphasises the value of clearly visualising and disseminating findings to a large audience. In order to communicate the findings of the 10-year trend analysis to patrons, legislatures, and other pertinent audiences, data visualisation tools, infographics, and reports are used. This makes sure that the results are useful, effective, and easily accessible so that they can guide the creation of policies and decisions pertaining to androgenetic alopecia. The results of this study may have substantial ramifications for academics, medical professionals, policymakers, and other industry participants. The research can lead future research paths, prioritise research areas, and suggest areas that require additional examination by highlighting key research challenges and pointing out gaps in the study of androgenetic alopecia. Identifying emergent study issues, analysing the changing patterns in the area, and establishing research strategies can all benefit from an analysis of word dynamics and correlations among terms. The results reveal hidden relationships and patterns, advance knowledge of the research environment in this area, and influence androgenetic alopecia research objectives and policies.

Can we spot fake public comments generated by ChatGPT(-3.5, -4)?: Japanese stylometric analysis expose emulation created by one-shot learning.

Public comments are an important opinion for civic when the government establishes rules. However, recent AI can easily generate large quantities of disinformation, including fake public comments. We attempted to distinguish between human public comments and ChatGPT-generated public comments (including ChatGPT emulated that of humans) using Japanese stylometric analysis. Study 1 conducted multidimensional scaling (MDS) to compare 500 texts of five classes: Human public comments, GPT-3.5 and GPT-4 generated public comments only by presenting the titles of human public comments (i.e., zero-shot learning, GPTzero), GPT-3.5 and GPT-4 emulated by presenting sentences of human public comments and instructing to emulate that (i.e., one-shot learning, GPTone). The MDS results showed that the Japanese stylometric features of the public comments were completely different from those of the GPTzero-generated texts. Moreover, GPTone-generated public comments were closer to those of humans than those generated by GPTzero. In Study 2, the performance levels of the random forest (RF) classifier for distinguishing three classes (human, GPTzero, and GPTone texts). RF classifiers showed the best precision for the human public comments of approximately 90%, and the best precision for the fake public comments generated by GPT (GPTzero and GPTone) was 99.5% by focusing on integrated next writing style features: phrase patterns, parts-of-speech (POS) bigram and trigram, and function words. Therefore, the current study concluded that we could discriminate between GPT-generated fake public comments and those written by humans at the present time.

Constructing the image of the USSR in traditional media (on the example of Argumenty i Fakty): Experience of statistical and qualitative analysis of communication strategies

The article defines the communication strategies of creating the image of the USSR on the basis of statistical and qualitative analysis of the Argumenty i Fakty content. The empirical basis of the article is based on the material of the newspaper published from 1992 to 2022. The use of statistical methods to analyze all issues (by continuous sampling) made it possible to identify the range of articles that mention the lexemes “USSR”, “Soviet Union”, “Union Soviet”, “country Soviet”. Communication strategies are determined by analyzing the frequency of mentioning certain word forms in the media content. The use of the thematic modeling algorithm, namely, the method of machine learning allowed us to analyze the corpus of documents, to detect patterns of words and phrases that occur most often and thus characterize the content of Argumenty i Fakty, and to automatically group articles in which terms close in meaning occur. Thus, 5757 articles were classified and 95 clusters were identified, which in turn were reduced to 16 large thematic blocks: sports, cinema, economy, music, memory, ideology, politics, education, space, debts and credits, products, man, war in Afghanistan, intelligence, atomic project. The most prominent images of the USSR in the newspaper Argumenty i Fakty appear to be the following: the winner in World War II and the geopolitical rival of the USA in the Cold War. Statistical analysis of the articles proves the importance of the Soviet experience for understanding Russia’s domestic and foreign policy. Qualitative discourse analysis shows the prevalence of positive representation of the Soviet era, which allows to label the USSR as a significant “other” for contemporary Russia. Argumenty i Fakty is characterized as an important agent/subject of the contemporary politics of symbolic identification. Keywords: image of the USSR, quantitative content analysis, Argumenty i Fakty, traditional media, communication strategies.

Frasa Verbal Bahasa Teon di Pulau Seram

This study aims to identify verbal phrases in the Teon language of Watludan Village, Seram Island, in Central Maluku District. Data collection was carried out using instruments containing Indonesian phrases which were then translated into Teon language phrases according to the data requirements. Data analysis was carried out by classifying all types of verbal phrases according to Teon language. After the data is classified, it is then analyzed based on the types of verval phrases by making formulas according to the phrase patterns of the Teon language. The results of the study show that the verbal phrases of Teon are divided into two, namely verbal phrases that can be divided into attributive endocentric verbal phrases and coordinative endocentric verbal phrases. Attributive endocentric verbal phrases consist of several types of phrases based on their attribute word classes, namely (1) verb phrases with the adverbial attribute of way, (2) verbal phrases with modal/aspect attributes, (3) verbal phrases with time averbial attributes, (4) verbal phrases with intensity attributes, (5) verbal phrases with negative attributes, (6) attribute verbal phrases in a negative way, (7) verbal phrases mean but adverbial ways; and (2) coordinate endocentric verbal phrases.

Colloquialism and genre variation in Chinese

Abstract Colloquialization has been identified as one of the most crucial change processes of the English language, through which written genres gradually shift towards spoken styles. Colloquialism refers to the synchronous features resulting from colloquialization. There is limited research into the lexical and phrasal patterns indicating colloquialism in Chinese or how Chinese colloquialism manifests in different spoken and written genres. To extract the lexical and phrasal patterns indicating colloquialism in Chinese, this study used a corpus-driven approach to determine and compare keywords and phrase frames from two training Chinese corpora and those keywords and phrase frames with higher frequencies were retained for further testing.

Prepositional Phrases in Deaf and Hearing Students’ Narratives: A Functional Perspective

Comprehending the linguistic features of deaf student writers in comparison to those of the hearing group at the further phase helps in providing pedagogical treatment for mitigating the linguistic gap between both. In line with this idealism, the present study aims to reveal the phenomena of prepositional phrase positions and patterns of deaf and hearing students in representing the circumstances of their real-life narratives. Four stories about being chased by a dog were elicited from each of the groups. The analysis was carried out by employing the table adapted from the experiential framework of Halliday and Matthiessen (2004). The findings in general reveal the less flexible positions of prepositional phrases and the less complex prepositional phrase patterns of the deaf group. Though the results of this study might not be absolutely conclusive, at least they portray the insight into the certain tendency between both groups of students in representing the circumstances for their meaning making of narratives. At the further phase, the findings of the present study are expected to provide benefits for language teachers and practitioners in the context of special education especially the case of deafness.

Verb-Noun Collocations In Newspaper Editorials In Ghana: A Corpus-Based Analysis

This paper is a corpus-based study which aims at profiling the most frequent verb-noun collocations and their communicative functions in newspaper editorials in Ghana. In all, a total of 92,927 running words were culled from 220 newspaper editorials from The Ghanaian Times and The Daily Graphic, which were published in the 2016 and 2017 news years, for compilation of a specialised corpus for the study. From the collocation tab of the AntConc corpus software, sixty-seven Verb-Noun Collocations were found to constitute the most frequently occurring collocations in the newspaper editorials under study. The corpus revealed that both predictive and open Verb-Noun Collocations which alternate at the left and right sides on the collocation window span are mostly used by newspaper editorial writers in Ghana. Again, it was observed that phrasal patterns of a noun collocate differed according to its position on either the left or right side of a verb node. The semantic prosodies of the profiled verb-noun collocations revealed five major discourses which constituted the most discussed issues in the newspaper editorials published in 2016 and 2017 news years.These issues were governance, politics and elections, peace and security, law and order, and corruption.

THE SYNTAX OF TAMIANG LANGUAGE ACEH PROVINCE

Syntax is the study of rules for combining words into a sentence. The purpose of this research is to find out how the syntax of Tamiang language especially the sentences that are construction in the Language such as the patterns of phrases, clauses, and sentences. This research used descriptive design. The data in this research were collected through interviewing, noting and translating, recording and transcribing all utterances of Tamiang language. The number of subjects were about 24 persons. This research resulted that Tamiang language has four kinds of phrases. Meanwhile, Tamiang clauses have two forms: complete and incomplete form. The complete form has two patterns: subject-predicate and predicate -subject. Based on the parts of speech, Tamiang clauses have five forms: nominal clause, verbal clause, adjectival clause, numeral clause and prepositional clause. There are four kinds of sentences in Tamiang language: declarative sentence, imperative sentence, interrogative sentence and exclamatory sentence.Key Words: Syntax, Sentences, and Tamiang Language

Interferensi Morfologi dan Sintaksis Bahasa Indonesia Terhadap Bahasa Jepang Pada Pembuatan Kalimat Pasif Bahasa Jepang oleh Pembelajar Bahasa Jepang

This study aims to explain the forms of morphological and syntactic interference of the Indonesian language in Japanese language learners' production of Japanese passive sentences. The data were then analyzed as errors because of the two interference. Research data was obtained through an open questionnaire distributed to Japanese Literature Study Program students during the 4th semester at Brawijaya University. Based on the research, the total forms of morphological interference found were 47. Morphological interference in the transfer of morphemes was the most common, namely 42 or 89%, followed by the removal of grammatical categories, as much as 4 or 9% and the most diminutive replicas of grammatical functions, as much as 1 or 2%.Meanwhile, syntactic interference is 33. The number of syntactic interference in phrase patterns is 22 or 69%, and syntactic interference in sentence patterns is 11 or 31%. Based on the data on the findings of morphological and syntactic interference in the Indonesian language, the form of errors following Parera's (1997) theory is 55. The number of 52 or 94% production errors is the highest, followed by a decrease of 2 or 4%; and the least is overproduction of 1 or 2%.

Wi-Phrase: Deep Residual-Multihead Model for WiFi Sign Language Phrase Recognition

Sign language (SL) is used by hearing impaired and deaf people. The WiFi-based sign language recognition (SLR) technology has attracted much attention due to its contactless nature and wide applications. Most previous SLR works are designed to recognize a single isolated sign word in data samples. However, such assumption is not realistic in practical applications, such as, in daily communication, deaf people usually express their mind through a phrase (a.k.a. a group of words) rather than an isolated word. Compared with the previous works, the sign words in a phrase have variety of length, sequential patterns, and combinations in realistic communications between deaf people. Therefore, it is challenging in exploiting the WiFi signals to accurately capture the unique patterns of SL in the previous natural setting. In this article, we propose Wi-Phrase, a multigesture context-awareness SLR system. Wi-Phrase exploits WiFi signals to translate SL to the English phrase. To achieve this, Wi-Phrase employs principal component analysis (PCA) projection to filter out the noise and convert cleaned WiFi signals to spectrogram. Then, we propose a novel Residual-MultiHead model that exploit residual learn structure to obtain local patterns of phrases and adopt multihead block to capture the global context information of phrase. To prove the advanced nature of our model, we design a WiFi-based SL phrase data set of 40 categories for experiments. Our comprehensive evaluation shows that Wi-Phrase achieves an accurate phrase recognition accuracy of 95.03%. In future, we envision Wi-Phrase could be widely used as the phrase command control system for deaf people in IoT devices.

Comparing lexical bundles across three advanced mathematical text types: a corpus-based genre-focused investigation

Although much research has been conducted on academic multi-word units across a range of academic disciplines, little attention has been given to the distribution of such units in mathematics. Drawing on three parallel corpora of doctoral dissertations, textbook chapters, and research journal articles, this study compares the use of recurrent bundles across three mathematical text types using a combination of corpus methodology and linguistic analysis. The corpus treatment of bundles is then supplemented with an analysis of their structural forms and discourse functions. A total of 291 four-word bundles that recur at least 25 times per million words, and appear in 15% or more of the texts were retrieved and their structural and functional attributes examined. Results indicate that student-produced texts tend to use fewer and less varied lexical bundles than research articles and educational texts. The three text types also show considerable variation in the use of structural forms of recurrent bundles, with the student writing incorporating more phrasal patterns, while expert texts employing clausal constructions. Functionally, the three text types exhibit striking similarities, making greater use of research- and text-oriented bundles but producing a much narrower range of participant-based bundles.

A Novel Hierarchical Document Clustering Framework on Large TREC Biomedical Documents

The growth of microblogging sites such as Biomedical, biomedical, defect, or bug databases makes it difficult for web users to share and express their context identification of sequential key phrases and their categories on text clustering applications. In the traditional document classification and clustering models, the features associated with TREC texts are more complex to analyze. Finding relevant feature-based key phrase patterns in the large collection of unstructured documents is becoming increasingly difficult, as the repository's size increases. The purpose of this study is to develop and implement a new hierarchical document clustering framework on a large TREC data repository. A document feature selection and clustered model are used to identify and extract MeSH related documents from TREC biomedical clinical benchmark datasets. Efficiencies of the proposed model are indicated in terms of computational memory, accuracy, and error rate, as demonstrated by experimental results.

Stress detection using natural language processing and machine learning over social interactions

Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Social media content is mostly used for review, opinion, influence, or sentiment analysis. In this paper, we aim to extend sentiment and emotion analysis for detecting the stress of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to accomplish sentiment analysis with the aid of machine learning algorithms and a deep learning model, BERT for sentiment classification. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method for scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us to predict which topic is linked to the textual data. With the aid of these models, we will be able to detect the emotion of users online. Further, these emotions can be used to analyze stress or depression. In conclusion, the ML models and a BERT model have a very good detection rate. This research is useful for the well-being of one's mental health. The results are evaluated using various metrics at the macro and micro levels and indicate that the trained model detects the status of emotions based on social interactions.

A corpus-based investigation on noun phrase complexity in L1 and L2 English writing

Since the 1990s, grammatical complexity has received considerable attention in different fields of L2 studies, for instance, second language acquisition (SLA) and L2 writing. While over the past three decades grammatical complexity has frequently been represented by clausal structures in L2 writing, since the beginning of the 2010s scholars have increasingly paid attention to phrasal features. We conducted this study to investigate how the use of noun phrases is associated with L1- and L2-English language backgrounds in academic writing. Noun phrase complexity was operationalized to the 11 noun modifiers (e.g., premodifying nouns, relative clauses, prepositional phrases) proposed in Biber, Gray and Poonpon (2011). A Chi-square test followed by a residual analysis was used to statistically analyze noun phrases in the two corpora. The results demonstrate that there is an association between the use of noun phrases and whether the author is an L1 or L2 user of English. The L1 essays have diverse patterns of noun phrases, whereas the L2 essays have compressed structures of noun phrases. A qualitative analysis of the corpora reveals repeated cases of phrasal modifiers in the L2 essays. Pedagogical implications are provided for academic writing courses for L1 and L2 students.

Peeking into the socio-historical background and current use of ‘me (no)likey’

The verbto likehas fallen repeatedly under the gaze of scholars. One aspect which has stimulated vigorous discussion is its original use in impersonal constructions and its later change of argument structure along with the disappearance of impersonals from English. Nonetheless, evidence from current informal English shows thatlikeis now used in constructions which bear a close resemblance to the older impersonals, although always displaying alternative spelling variants, especiallylikey. This paper seeks to further our understanding of the verbto like, focusing specifically on these new constructions. To this end I will uselikeyas a generic label to refer to such new uses and constructions, regardless of variations in spelling (unless otherwise stated). Using data from theCorpus of Historical American EnglishandiWeb Corpus, the study will seek to answer the following research questions:RQ1.What are the morpho-syntactic features of the expression ‘me (no) likey’ in Present-Day English?RQ2.What is the origin of the sequence ‘me (no) likey’?RQ3.Where do phrasal patterns withlikeyfall on the continuum of idiomaticity (Michaelis, 2017)?

From words to phrases: neural basis of social event semantic composition

Events are typically composed of at least actions and entities. Both actions and entities have been shown to be represented by neural structures respecting domain organizations in the brain, including those of social/animate (face and body; person-directed action) versus inanimate (man-made object or tool; object-directed action) concepts. It is unclear whether the brain combines actions and entities into events in a (relative) domain-specific fashion or via domain-general mechanisms in regions that have been shown to support semantic and syntactic composition. We tested these hypotheses in a functional magnetic resonance imaging experiment where two domains of verb-noun event phrases (social-person versus manipulation-artifact, e.g., “hug mother” versus “fold napkin”) and their component words were contrasted. We found a series of brain region supporting social-composition effects more strongly than the manipulation phrase composition—the bilateral inferior occipital gyrus (IOG), inferior temporal gyrus (ITG) and anterior temporal lobe (ATL)—which either showed stronger activation strength tested by univariate contrast, stronger content representation tested by representation similarity analysis, or stronger relationship between the neural activation patterns of phrases and synthesis (additive and multiplication) of the neural activity patterns of the word constituents. No regions were observed showing evidence of phrase composition for both domains or stronger effects of manipulation phrases. These findings highlight the roles of the visual cortex and ATL in social event compositions, suggesting a domain-preferring, rather than domain-general, mechanisms of verbal event composition.

Multi-view self-attention networks

Self-attention networks (SANs) have attracted an amount of research attention for their outstanding performance under the machine translation community. Recent studies proved that SANs can be further improved by exploiting different inductive biases, each of which guides SANs to learn a specific view of the input sentence, e.g., short-term dependencies, forward and backward views, as well as phrasal patterns. However, less studies investigate how these inductive techniques complementarily improve the capability of SANs and this would be an interesting question to be answered. In this paper we selected five inductive biases which are simple and not over parameterized to investigate their complementarily. We further propose multi-view self-attention networks, which jointly learn different linguistic aspects of the input sentence under a unified framework. Specifically, we propose and exploit a variety of inductive biases to regularize the conventional attention distribution. Different views are then aggregated by a hybrid attention mechanism to quantify and leverage the specific views and their associated representation conveniently. Experiments on various translation tasks demonstrate that different views are able to progressively improve the performance of SANs, and the proposed approach outperforms both the strong Transformer baseline and related models on Transformer-base and Transformer-big settings. Extensive analyses on 10 linguistic probing tasks verify that different views indeed tend to extract distinct linguistic features and our method gives highly effective improvements in their integration.11We release the code of this work in https://github.com/NLP2CT/MV-Attn.

XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

The digital India program encourages Indian citizens to become conversant with e-services which are primarily English language-based services. However, the vast majority of the Indian population is comfortable with vernacular languages like Bengali, Assamese, Hindi, etc. The rural villagers are not able to interact with the Relational Database Management system in their native language. Therefore, create a system that produces SQL queries from natural language queries in Bengali, containing ambiguous words. This paper proposes a Bengali Query Processor named Extended Bengali language Query Processing System (XBLQPS) to handle queries containing ambiguous words posted to a Healthcare Information database in the electronic domain. The Healthcare Information database contains doctor, hospital and department details in the Bengali language. The proposed system provides support for the Bengali-speaking Indian rural population to efficiently fetch required information from the database. The proposed system extracts the Bengali root word by removing the inflectional part and categorizing them to a specific part of speech (POS) using modified Bengali WordNet. The proposed system uses manually annotated parts of speech detection of a word based on Bengali WordNet. Patterns of noun phrases are generated to detect the correct noun phrase as well as entity and attribute(s). Entity and attributes are used to prepare the semantic table which is utilized to create the Structured Query Language (SQL). The simplified LESK method is utilized to resolve ambiguous Bengali phrases in this query processing system. The accuracy, precision, recall and F1 score of the system is measured as 70%, 74%, 73%, and 73% respectively.