Similar Words Research Articles

Infrathin: An Experiment in Micropoetics

Infrathin: An Experiment in Micropoetics

Investigation on Picture Books Reading Education in Primary School <br/>—Taking H Province for Example

Picture book reading facilitates the acquisition of reading literacy. How picture books are used in primary school education and whether picture book reading foster students’ learning ability are the two questions to be discussed in the passage. According to the investigation, lack of time, difficult in choosing picture books and the renovation of teaching methods are the main problems exist. In order to promote the reading literacy: teachers should categorize picture books or stories with the same or similar topics, grammar, words and sentence structure in their ordinary teaching; Teachers or parents should know a general form of asking questions to enhance the effect of students’ in picture book reading and improve students’ reading literacy.

Indian News Headlines Classification using Word Embedding Techniques and LSTM Model

Newspapers introduce us to the latest happenings around the world. Going paperless creates more opportunities for newspapers, like broadcasting news coverage and presenting breaking news conveniently. News headlines are considered under the short text category and are vibrant subjects for researchers. Creating a dense vector from short texts has become a challenging and essential task in many applications such as recommender systems, context analysis, decision making, text classification, etc. This work not only targeted creating a classification model for the short text but also categorized the headlines with the ‘unknown’ category. Our work uses Bidirectional Encoder Representations from Transformers (BERT), cosine similarity index, word embedding, and Long Short-Term Memory (LSTM) network to classify news headlines in multiple categories. Our proposed method outperforms labeling the unlabeled data with the help of a BERT sentence encoder. The system uses LSTM to learn the headlines as input vectors and classify the headline text by the classifier. At the end of this experiment, the designed pipeline achieves remarkable precision at the class level.

СЕМАНТИКА И ФУНКЦИОНИРОВАНИЕ СУЩЕСТВИТЕЛЬНОГО «ДИЛЕТАНТ» В СОВРЕМЕННОМ РУССКОМ ЯЗЫКЕ

The article tests the peculiarities of the meaning and usе of the noun dilettante in Russian language in different spheres of communication. The analysis includes the information about the word in various explanatory, synonymous and antonymic dictionaries of the Russian language, the noun's functioning in contexts based on the website of «National Corpus of the Russian Language» and in reflexive expressions used by internet users. The article presents similar words to the noun dilettante, and also analyzes the evaluation of the studied word in corpus materials, as well as in the language consciousness of native Russian speakers. The work uses descriptive method, component analysis of meaning method, distributive method, continuous sampling of material from dictionaries, and targeted sampling of related contexts from the «National Corpus of the Russian Language». As a result of the analysis, the descripition of the man known as dilettante is created, moreover, the ambivalence of the tested noun dilettante is identified. The conclusions obtained in the course of this research can be used to the lessons on Cultural linguistics, the practice of teaching Russian as a foreign language, and also to the development and compilation of educational lexicographic dictionaries.

Applying Distributional Semantic Models to a Historical Corpus of a Highly Inflected Language: the Case of Ancient Greek

So-called “distributional” language models have become dominant in research on the computational modelling of lexical semantics. This paper investigates how well such models perform on Ancient Greek, a highly inflected historical language. It compares several ways of computing such distributional models on the basis of various context features (including both bag-of-words features and syntactic dependencies). The performance is assessed by evaluating how well these models are able to retrieve semantically similar words to a given target word, both on a benchmark we designed ourselves as well as on several independent benchmarks. It finds that dependency features are particularly useful to calculate distributional vectors for Ancient Greek (although the level of granularity that these dependency features should have is still open to discussion) and discusses possible ways for further improvement, including addressing problems related to polysemy and genre differences.

Audio delivery of health information: An NLP study of information difficulty and bias in listeners

Health literacy is the ability to understand, process, and obtain health information and make suitable decisions about health care [3]. Traditionally, text has been the main medium for delivering health information. However, virtual assistants are gaining popularity in this digital era; and people increasingly rely on audio and smart speakers for health information. We aim to identify audio/text features that contribute to the difficulty of the information delivered over audio. We are creating a health-related audio corpus. We selected text snippets and calculated seven text features. Then, we converted the text snippets to audio snippets. In a pilot study with Amazon Mechanical Turk (AMT) workers, we measured the perceived and actual difficulty of the audio using the response of multiple choice and free recall questions. We collected demographic information as well as bias about doctors’ gender, task preference, and health information preference. Thirteen workers completed thirty audio snippets and related questions. We found a strong correlation between text features lexical chain, and the dependent variables, and multiple choice response, percentage of matching word, percentage of similar word, cosine similarity, and time taken (in seconds). In addition, doctors were generally perceived to be more competent than warm. How warm workers perceive male doctors correlated significantly with perceived difficulty.

Adaptive Huffman Algorithm for Data Compression Using Text Clustering and Multiple Character Modification

Adaptive Huffman algorithm is a popular data compression technique that creates a variable-length binary code for each symbol in a message. However, the original algorithm may not be efficient in compressing text data, particularly when dealing with long sequences of repeated characters. In this study, we propose a novel approach to enhance the compression ratio of the Adaptive Huffman algorithm by utilizing text clustering and multiple character modification. The proposed method first clusters the text data into groups of similar words or phrases. Then, it modifies multiple characters in each group to reduce redundancy and increase the frequency of the most common characters. This modification enables the Adaptive Huffman algorithm to produce shorter codes for the modified characters and effectively compress the clustered text data. Experimental results on a benchmark dataset show that the proposed method achieves better compression ratios than the traditional Adaptive Huffman algorithm and other state-of-the-art compression methods. The proposed method can be applied to various text data, such as documents, emails, and chat messages, and can significantly reduce storage and transmission costs.

Methods of eliminating homonymy within different, grammatically similar word groups

The problem of automatic processing of natural language remains relevant for more than half a century. One of the important problems in the field of NLP is the creation of a semantic analyzer, which in turn goes through a number of steps. Determining homonymy is important in the semantic analysis of sentences. A method based on rules, a method based on statistical data, and methods based on machine learning are also used to determine homonymy. Statistical methods are mainly used to determine homonymy between grammatically similar word groups. In this article discusses the use of homonymy between two grammatically similar nouns and adjectives using statistical methods, namely Frequency and Bayesian methods. If bigrams and trigrams are used in the Bayesian method, the characteristics of word groups are classified in the frequentist method, and the parameters that can distinguish them are determined. The identified parameters are converted into numbers as a result of observations and probabilistic decisions are made.

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.

Assessing the Translatability of Emotional Discourse in Djaïli Amadou Amal’s Munyal “Les Larmes de La Patience

Human beings experience all sorts of emotions: sadness, fear, joy, anger, etc. which they communicate to their surrounding through language. As such, emotions are embedded in culture and thus differ from one person to another according to the cultural context and specific situations or events that trigger them. They can be expressed in action, for instance boxing in order to evacuate once anger, or in writing through language. Language is thus relevant for emotion and plays a key role in expressing emotions. There thus exists a close link between emotion and language. This partly explains why literary works, be it drama, prose or poetry are highly characterised by emotional discourse. This is because writers in order to educate, to sensitise, to entertain or to get the support of readers on an issue, evoke their emotions by the use of linguistic tools such as tone, diction, figurative languages, etc. However, despite the pervasiveness of emotions in literary texts, the translation of emotional discourse remains a challenging task. The reason being that, although emotions have been broadly searched in the domain of psychology, it remains under investigated in the domain of translation studies. As compared to other fields where the areas of investigation of emotions are types of emotions, emotions vocabulary in a specific culture, the measurement of the intensity of emotions, the cross-cultural similarities of emotion words, emotion meaning across culture, emotional problems, the manifestation of emotions, and so on, most translation scholars fail to investigate translation strategies which can be suitable to render emotions conveyed in a text. Some of these scholars limit themselves to the difficulties posed by the translation of emotions, and to finding equivalence in emotion words that exist between languages. This study thus aims at investigating translation strategies suitable to render the meaning of emotional discourse embedded in a literary work and their effects in the translation process, while focusing on the stylistic signals of emotions in a literary work more precisely in Djaïli Amadou Amal’s Munyal: Les Larmes de la Patience, and their translatability. It thus provides grounds for the translation of emotions, and comes up with translation strategies adapted for their translation. It also sheds light on what should be considered as the translation unit during their rendering. For this purpose, the emotions concerned in this study are anger, fear and sadness; and the selected emotional cultural phenomena dealt with in this research cut across borders. Besides, the study was grounded on the polysystem theory backed up by few literary and language theories. Also, it opted for a mixed approach. This approach enabled the collection of qualitative data through documentary research and textual analysis carried out both at the macro-textual level and at the micro-textual level; and quantitative data through statistics. The results of the investigation showed that stylistic signals of emotions in a literary text are dialogue, the author naming and describing emotions experienced by characters, characters’ choice of words, the description of characters’ facial expression, characters’ tone, the narrator tone and characters’ attitude. In addition to this, the study also revealed that translation strategies proposed by Chesterman (1997) more precisely literal translation, information change and explicitness change can be used to render the meaning of emotional discourse
 embedded in a text. These strategies can also be combined and used. Furthermore, the investigations also highlighted that in translating emotional discourse, a translator can either be “source-oriented” or “target-oriented”; or both. Given that both source and target-oriented strategies were used. Besides, the results also showed that in translating emotional discourse the translator should take into account sentences in their entirety and not isolated terms.

Paradigm gaps are associated with weird “distributional semantics” properties

Abstract This study investigates the phenomenon of defectiveness in Russian case and number noun paradigms from the perspective of distributional semantics. We made use of word embeddings, high-dimensional vectors trained from large text corpora, and compared the observed paradigms of nouns that are defective in the genitive plural, as suggested by Zaliznjak (1977), with the observed paradigms for non-defective nouns. When the embeddings of about 20,000 inflected forms were projected onto a two-dimensional space, clusters of case and number within case were found, suggesting global semantic similarity for words with the same inflectional features. Moreover, defective lexemes were characterized by lower semantic transparency, in that inflected forms of the same lexeme are semantically less similar to each other, and their meanings are also more idiosyncratic. Furthermore, compared to non-defective lexemes, inflected forms from defective lexemes are further away from the idealized average case-number meanings, obtained by averaging over the vectors of all inflected forms of the same case-number combination. As a consequence, the semantics of defective forms are predicted less precisely by a simple model of conceptualization that assumes that the meaning of a given Russian inflected form is approximated well by the sum of pertinent embeddings of the lexeme, case, and number within case. We conclude that the relationship between defectiveness and semantics, at least the kind captured by word embeddings, is stronger than has been anticipated previously.

(RE)THINKING THERMALISM IN EUROPE FROM THE STRATEGIC COMMUNICATION PERSPECTIVE: A THEORETICAL REFLECTION

This research aims to present a conceptual approach to the strategic importance of communication in the thermal sector. This therefore seeks to identify and analyse the scientific publications addressing the thermal sector from the strategic communication perspective. Furthermore, this also sets out the objectives and conclusions of the studies and conveys their relationship with communication. This study correspondingly applies a Systematic Literature Review (SLR) in keeping with the PRISMA Statement method covering the Scopus and Web of Science databases with 27 articles incorporated into the qualitative analysis. The results demonstrate that the three clusters obtained via the similarity of words enable the identification of the features that frame strategic communication as an essential tool for leveraging and promoting thermalism. This study contributes to identifying strategies and operationalised guidelines impacting on the publications able to outline promising future research paths for better knowledge about thermal tourism in Europe.

THE OUTLINE OF CONTEMPORARY ENGLISH AND UKRAINIAN СOMPUTER SLANG LEXICON

This article addresses computer English and Ukrainian computer slang as a lexical subsystem evoked to provide the Internet community with effective communication means. Slang lexicon fulfills a number of functions depending on the contextual features, level of professionalism of language users and their interest. Although it is argued by some scholars that slang words rarely undergo semantic changes, it has been revealed that in the process of adaptation in the target language lexical and grammatical transformations typically occur in computer slang. In particular, English computer slang words are adapted to the Ukrainian reality through the process of clipping, blending, adding inflections, conceptual metaphor and other figurative transfers of meaning. Interestingly, it is common for new forms to acquire connotations which are not found in the original language. Ridiculous associations help users to quickly connect the form and denotative meaning of a slang lexeme in question. The sources of computer slang formation include calque, telescopy, phonetic mimicry and translation. The knowledge of language community can significantly simplify the identification of hidden senses and related connotations in other similar words. Stratification of slang is a difficult task as well as its software implementation in online dictionary developments. The limitations of this research are related to complexity of establishing concise etymology and trace all relevant contextual features of slang units in the process of quickly evolving computer mediated communication.

HiBERT: Detecting the illogical patterns with hierarchical BERT for multi-turn dialogue reasoning

Dialogue reasoning is a new task beyond the traditional dialogue system, because it requires recognizing not only semantic relevance but also logical consistency between the candidate response and the dialogue history. Like “all happy families are happy alike, all unhappy families are unhappy in their own way”, various illogical patterns exist in the data. For example, some candidate responses use many similar words but with contradicted meanings with history; while some candidates may employ totally different words but convey consistent meanings. Therefore, an ideal dialogue reasoning model should gather clues from both coarse-grained utterance-level and fine-grained word-level to determine the logical relation between candidates and the dialogue history. However, traditional models mainly rely on the widely used BERT to read all the history and candidates word by word but ignore the utterance-level signals, which cannot well capture various illogical patterns in this task. To tackle this problem, we propose a novel Hierarchical BERT (HiBERT) to recognize both utterance-level and word-level illogical patterns in this paper. Specifically, BERT is firstly utilized to encode the dialogue history and each candidate response as the contextualized representation. Secondly, hierarchical reasoning architecture is conducted with this contextualized representation to obtain the word-level and the utterance-level attention distributions, respectively. In detail, we utilize the word-grained attention mechanism to obtain the word-level representation, and propose two different types of attention function, i.e, hard attention and soft attention, to obtain the utterance-grained representation. Finally, we fuse both the word-grained representation and the utterance-grained representation to calculate the logical ranking scores for the given candidate. Experimental results on two public dialogue datasets show that our model obtains higher ranking measures than the widely used BERT model, validating the effectiveness of hierarchical reading of HiBERT. Further analysis on the impact of context length and attention weights shows that the HiBERT actually has the ability to recognize different illogical patterns.

Similar words in Russian language: Paronyms

Dilde var olan sözler bir dilbilim dalı olan sözcük bilimi tarafından incelenir. Sözcük biliminin önemli konularından olan sözcüksel araçlar; çok anlamlılık, eş anlamlılık, sesteşlik, zıt anlamlılık, mecaz, yan anlamlılık vb. dilin kelime hazinesinin zenginleştirdiği gibi kelime bilgimizi de zenginleştirerek genişletir ve aynı zamanda o dilde konuşma becerimizi artırır. Sözcük biliminin ayrılmaz parçası olan kelime oluşumu kategorisi ise diller için esastır. Kelime oluşumu kategorisi söz varlığını çeşitlendirerek zenginleştirir. Bazen dilde benzer gibi görünen kelimeler ses, yapı bilimsel ve en önemlisi anlam olarak birbirinden farklılık gösterir. İlk bakışta benzer gibi görünen bu kelimeler birbirinin yerine kullanılamayan kelimeler grubudur. Rus dilinin de önemli bir kısmını oluşturan bu tür kelimeler, ses olarak benzer ancak anlam bakımından kısmen veya tamamen farklılık göstermektedir. Bu tür kelimeler Rusçada paronim (Rus. пароним) olarak adlandırılmaktadırlar. Çağdaş Rus dil bilgisinde paronimler, ses olarak birbirine benzer ancak anlam olarak eşleşmeyen kelimeler olarak tanımlanır. Kendi aralarında bir çift oluşturan bu tür kelimeler genellikle kullanımda hatalara yol açar. Paronim kelimelerin anlamlarını karıştırmadan cümlede doğru bir şekilde kullanmak, aktarmak istediğimiz fikri anlaşılır kılmaktadır. Bu kelimelerin anlamlarını ayırt etmenin erişilebilir yolu sözlüklerdir. Rusçada paronim sözlüklerin varlığı bu dilde bu kelimelerin çokluğunu göstermektedir. Bu makalede, Çağdaş Rus dilinin söz varlığının önemli bölümünü oluşturan paronimler, başka bir deyişle benzer sözcükler ele alınmaktadır. Öncelikle Rusçadaki paronimlerin genel yapılarının, hangi anlamlı sözler grubunda çoğunluk oluşturduğu daha sonra ise paronim kelimelerin cümlede anlamsal alanı nasıl etkilediği üzerinde durulacaktır. Aynı zamanda Rusçada kullanılan paronim sözlüklere ve paronomaziya kelimelerinin paronim sözcüklerden ayıran özelliklere değinilecektir. Bu makalede ele alınan konudaki amaç; Rusçadaki paronimlerin yazılışlarına, telaffuzlarına ve onlarla birlikte kullanılan kelimelere dikkat edilmesi gerektiğidir.

An analysis of cognitive change in online mental health communities: A textual data analysis based on post replies of support seekers

The replies of people seeking support in online mental health communities can be analyzed to discover if they feel better after receiving support; feeling better indicates a cognitive change. Most research uses key phrase matching and word frequency statistics to identify psychological cognitive change, methods that result in omissions and inaccuracy. This study constructs an intelligent method for identifying psychological cognitive change based on natural language processing technology. It incorporates information related to emotions that appears in reply text to help identify whether psychological cognitive change has occurred. The model first encodes the emotion information based on rule matching and manual annotation, then adds the encoded emotion lexicon and a cognitive change lexicon to a word2vec high-dimensional semantic word vector training, converts the annotated cognitive change recognition text into a vector matrix using the trained model, and train in the annotated text using TextCNN. To compare the results with those of the traditional methods (key phrase matching and sentiment word frequency statistics), this study uses a semi-automated approach to construct a lexicon of psychological cognitive change, as well as a keyword lexicon without cognitive change, based on word vectors and similarity. We compare the performance of the classifier before and after the fusion of the graphical emotion information, compare the LSTM and Transformer as baselines, and compare traditional word frequency statistics methods. The experimental results show that our proposed classification model performs better than the others; it achieves 84.38% precision, an 84.09% recall rate, and an 84.17% F1 value. Our work bears methodological implications for online mental health platforms.

"Once you get cancer you die. There is no way to get saved from cancer." A qualitative exploration of patients' perceptions towards cancer in Fiji.

Understanding patients' perspective to get an insight into cancer, and how best the public health systems can battle with this disease is the way forward in this current world. This study aimed to explore patients' knowledge about common cancers, barriers to assessing cancer information and cancer preventative approaches in Fiji. The study used a qualitative method approach that was conducted among patients who attended Special Outpatients (SOPD) at the four selected health centres in Lautoka Subdivision, Fiji from 1st March to 30th April 2021. A semi-structured open-ended questionnaire was used to guide in-depth interviews. These audio recordings were transcribed and analysed using thematic analysis. All interview transcripts were read and similar words and phrases were assigned numbers which were grouped together to identify themes and sub themes. Twenty-eight patients took part in the in-depth interview and the responses were grouped into four themes including; cancer knowledge, diagnosis of cancer in a close friend/family, barriers of communication and optimizing cancer awareness. Patients' awareness about common cancers and cancer risk factors was low. Many barriers for cancer screening were highlighted including stigmatization, fear, worry, death, lack of information, herbal medicine use, lack of resources and delay in diagnosis. Awareness strategies highlighted by participants included community outreach programs, house to house visits, opportunistic screening, engagement of community health care workers and the concept of a cancer hub centre. It is evident that there is a range of views from patients towards cancer and it is important to understand these perceptions to better guide public health interventions concerning cancer. This puts more focus on the need to invest more in information, education, and communication material for public campaigns that target a variety of people for a wider reach.

The Golden Rule and Ancient Jewish Biblical Exegesis: The Pluriformity of a Tradition

Abstract The present article is an integrative study of rabbinic sources in tandem with gospel pericopes, Pauline passages, and a passage of the Duae viae (included in the Didache). I contend that all these sources are variants of a Jewish tradition composed of two elements: (a) a maxim—the golden rule—or an equivalent biblical verse, Lev 19:18b; (b) an assertion that this maxim or verse summarizes the torah, or is the core of the whole torah (the difference in Hebrew is between kǝlāl, “rule,” and kōl, “whole”). The wording of these variants cuts across the putative division between the various corpora. Observing the pluriformity of this tradition is important for the study of both the New Testament and rabbinic literature, the focus of the present article. This study demonstrates that early phraseology may be preserved in late rabbinic texts, which thus problematizes the “synoptic problem” of rabbinic literature (and, mutatis mutandis, of the gospels). Similar wording should not blur different contents; affinities should not obscure inherent dynamics. The potential for a radical interpretation of the formulation that “the whole torah” is encapsulated in one verse or maxim has not been realized in rabbinic literature; the radical potential of the formulation was realized and developed by Paul in Gal 5:14.

Input-related demands: vocoded sentences evoke different pupillometrics and subjective listening effort than sentences in speech-shaped noise

Objectives The Framework for Effortful Listening (FUEL) suggests five input-related demands can alter listening effort: source, transmission, listener, message and context factors. We hypothesised that vocoded sentences represented a source factor degradation and sentences in speech-shaped noise represented a transmission factor degradation. We used pupillometry and a subjective scale to examine our hypothesis. Design Participants listened to vocoded sentences and sentences in speech-shaped noise at several difficulty levels designed to produce similar word recognition abilities; they also listened to unprocessed sentences. Within-participant pupillometrics and subjective listening effort were analysed. Post-hoc analyses were performed to examine if word recognition accuracy differentially influenced pupil responses. Study samples Twenty young adults with normal hearing. Results Baseline pupil diameter was significantly smaller, peak pupil dilation was significantly larger, peak pupil dilation latency was significantly shorter, and subjective listening effort was significantly greater for the vocoded sentences than the sentences-in-noise. Word recognition ability also affected pupillometrics, but only for the vocoded sentences. Conclusions Our findings suggest that source factor degradations result in greater listening effort than transmission factor degradations. Future research should address how clinical interventions tailored towards different input-related demands may lead to reduced listening effort and improve patient outcomes.

Franco…qui ? Franco…quoi ? Considérations lexicales au niveau débutant ou quelques questions provocantes sans réponses définitives

Franco...who? Franco...what? Lexical considerations at beginner level or some provocative questions without definitive answers “Francophones”, “francographes”, “francophiles”, “francophobes”... What an extraordinary word-formation richness! Not to mention similar words, more complicated and difficult to translate : “francology” (scientific discipline), “francité” (“frenchness”), “francogène” (“of francophone origin”). Although they all contain the same prefix “franco-”, all these concepts introduce a sense of inconsistency: for some of them refer to the world of francophony, while others are closely related only to France or its inhabitants. Why? Is it correct and fair? What good/bad comes from this confusion? This is an issue that the author ponders on, without giving obvious answers, but rather provoking to pose further disturbing questions about the identity of contemporary and future francophony.