Word Position Research Articles

The neurophysiological mechanism of valence-space congruency effect: evidence from spatial Stroop task and event-related EEG features.

Metaphors commonly represent mental representations of abstract concepts. One example is the valence-space metaphor (i.e., positive word-up, negative word-down), which suggests that the vertical position of positive/negative words can modulate the evaluation of word valence. Here, the spatial Stroop task and electroencephalography (EEG) techniques were used to explore the neural mechanism of the valence-space congruency effect in valence-space metaphors. This study showed that the reaction time of the congruent condition (i.e., positive words at the top and negative words at the bottom of the screen) was significantly shorter than that of the incongruent condition (i.e., positive words at the bottom and negative words at the top of the screen), while the accuracy rate of the congruent condition was significantly larger than that of the incongruent condition. The analysis of the amplitudes of event-related potential components revealed that congruency between the vertical position and valence of Chinese words could significantly modulate the amplitude of attention allocation-related P2 component and semantic violations related N400 component. Moreover, statistical tests conducted on the post-stimulus inter-trial phase coherence (ITPC) found that the ITPC value of an alpha band region of interest (8-12Hz, 100-300 ms post-stimulus) in the time-frequency plane of the congruent condition was significantly larger than that of the incongruent condition. Above all, the current study proved the existence of the space-valence congruency effect in Chinese words and provided some interesting neurophysiological mechanisms regarding the valence-space metaphor.

Protracted phonological development of a Portuguese six-year-old from the perspective of nonlinear phonology

ABSTRACT 1 1 is also available in European Portuguese. As a contribution to a special issue with individual profiles in protracted phonological development (PPD), we present a European Portuguese-speaking six-year-old, “Vicente”. By age six years, Portuguese-learning children have generally mastered most of the phonology. However, Vicente showed severe persisting PPD, which was negatively impacting his general socialisation. Although word length and stress often matched the adult targets as did many consonants and vowels, consonants were restricted in distribution and sequences. Consonant clusters showed a particularly high proportion of reduction. A constraints-based nonlinear phonological analysis led to a proposed intervention plan to address needs across the phonological hierarchy in the context of his greater need for enhanced socialisation: starting with the more attainable new word positions and sequences for consonants in his inventory, before addressing a major need for new word structure (clusters) and minor needs for segmental development (additional coronals).

Word in technogenic multidimensional space

Today, artificial intelligence is actively mastering natural languages, becoming an interlocutor and partner of human in various aspects of activity. However, the symbolic approach, which implies the transfer of rules and logic, has failed, the number of rules and exceptions of the language does not allow its formalization, so modern «deep learning» of artificial neural networks involves an independent search for patterns in extensive databases. During training, artificial intelligence puts a word into a sentence so that the syntagmatic relationships are as close as possible to those of the target word in the base, taking into account both the semantic relationships of words and the relationships between words in the sequence of presentation. The «language» of information technologies is digital. During natural language processing, words are represented in vector form as a sequence of numbers. The idea of representing words mathematically is familiar to people and is usually associated with logical consistency. Visualization of the position of words in a multidimensional space created by artificial intelligence demonstrates a number of patterns, obvious semantic and syntactic relationships, but the essence of other relationships between words is not obvious. The mathematical representation of words, created by artificial intelligence, can allow you to look at the language from a new non-human point of view.

Idiosyncratic consonant clusters: a Farsi-speaking child with protracted phonological development

ABSTRACT This study presents a nonlinear phonological analysis of speech data from a Farsi-speaking child with protracted phonological development (aged 4;8) with very low accuracy on consonants. Results revealed some common phenomena (fricatives produced as stops; dorsals and non-anterior coronals produced as anterior coronals) and some uncommon phenomena (nasals produced as oral stops; voicing and devoicing of singleton obstruents in all word positions). These phenomena interacted in word-medial clusters to create an unusual sequence of two anterior-coronal or two bilabial stops, with C1 voiced and C2 voiceless, clusters which do not occur in the basic phonology of Farsi spoken by adults. We present a non-linear constraints-based analysis of the child’s speech production and a plan for intervention targeting the child’s special difficulties.

Chamling and English Question Words: A Comparative Stud

This study aims to explore the questions words of Chamling and analyze the question words of Chamling and English. For this purpose Chamling and English data related to the question words are used. The comparative and contrastive study of data reveals that the position of question words is different in Chamling—initial, middle, and final—and in English—only initial. The position of Chamling question words contrasts with English question words in interrogative sentence. The finding of study shows that question words are used to ask open questions in both languages.

LEARNING NAHWU FOR ARABIC STUDENTS IN ISLAMIC COLLEGES: EXPERTS' PERSPECTIVES / تعليم النحو لطلاب تخصصات اللغة العربية فى الجامعات الإسلامية من وجهة نظر الخبراء

This study aims to find out experts’ point of view about the reality of Nahwu learning by Arabic students in Islamic Colleges. Qualitative method was used in this research. The data were collected by interviewing several experts in Arabic language. Results showed that the purpose of Nahwu learning is not only to have the ability in understanding nahwu, but also to apply it in Arabic texts and to communicate; the characteristics of nahwu materials should be easy, simple, realistic, communicative, in stages, cognitive and functional; the method of learning is group work, peer learning, integrative texts, active learning, and communicative method; the media for learning are audio, visual, and audio visual; evaluation is done by implementing nahwu rules to construct and transform sentences, giving syakl on texts, and explaining the position of words in sentences. According to the experts, the ideal nahwu learning will help students to develop the four language skills.

Towards a processing model for argument-verb computations in online sentence comprehension

The current study investigated the processing stages by which the parser incorporates different pieces of information, from clausehood to argument roles, to update predictions about the main verb. Using Mandarin to match word position across relevant conditions, we extend classic ERP findings on the impact of argument role reversals ([The millionaireSUBJECT the servantOBJECTfired] vs. #[The servantSUBJECT the millionaireOBJECTfired]), by investigating cases where one of the nouns is not an argument of the verb ([The millionaireSUBJECT the servantOBJECTfired] vs. #[The millionaire thought [the servantSUBJECTfired…]]). The pattern of N400 responses suggest a three-stage model of argument-verb computation: An initial stage demonstrates sensitivity at the verb to semantic association only. Soon after, responses show partial structure-sensitivity, differentiating whether the noun phrases are arguments of the upcoming verb or not. Only at the last stage do the arguments’ roles (e.g. agent/patient) become available to impact computations at the verb.

Positions of Words and Logical Forms

Positions of Words and Logical Forms

On question words in Czech Sign Language

Abstract This paper investigates content questions and the paradigm of question words in Czech Sign Language (český znakový jazyk – ČZJ). While this topic has been investigated for a fair number of other sign languages, a comprehensive study for ČZJ is still missing. Our aim is to fill this gap. In the absence of a representative corpus for ČZJ, we use data from the most extensive electronic dictionary database (Dictio), developed at the Masaryk University in Brno. We offer a basic description of the ČZJ interrogative strategies for content questions. In doing so, we also situate the language within the typology of sign language interrogatives: we classify ČZJ as a language with a rich paradigm of question words, and we identify the basic syntactic position of question words as sentence-final. Furthermore, we outline three morphological types of ČZJ question words: simple, complex, and derived.

An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

Text representation of social media is an important task for users' sentiment analysis. Utilizing the better representation, we can accurately acquire the real semantic information expressed by online users. However, existing works cannot achieve the best results. In this paper, we construct and implement a sentiment analysis model based on the improved BERT and syntactic dependency. Firstly, by studying the word embeddings of BERT, we have ameliorated the embeddings representation. Attention mechanism is added to the word embeddings, sentence embeddings, and position embeddings. Secondly, we have exploited the dependency syntax analysis of the text, and the dependency relationship of different syntactic components will be obtained. For different syntactic components, the hierarchical attention mechanism is used to construct the phrase embeddings or block embeddings. Finally, we splice the syntactic blocks for sentiment analysis. Extensive experiments show that the proposed model has a stronger ability than the baselines on two standard data sets.

Acquisition of emphatic consonants by Ammani Arabic-speaking children

ABSTRACT This article investigates the production of the emphatic consonants /ṭ, ḍ, ṣ/ by typically developing Jordanian children. Sixty typically developing monolingual Ammani Arabic-speaking children (30 boys and 30 girls) with ages ranging from 2 to 7;11 years were recruited in a production experiment. In the experiment, they were asked to produce 18 minimal pair words with emphatic consonants and their corresponding plain coronals in all word positions. Contrary to Amayreh & Dyson (1998) and Hamdan & Amayreh (2007), >50% of Ammani Arabic-speaking children produced emphatic consonants by the age of 3, >75% produced them by the age of 4, and >90% produced them by the age of 5. Acoustically, they produce them in an adult-like manner at the age of 6 word-initially and medially and at the age of 7 word-finally. The acoustic measurements confirm that children’s productions become increasingly adult-like with age. Compared with nonemphatic consonants, the appearance of emphatics tends to be delayed in Ammani Arabic. This delay in the production of emphatics, which are among the least frequently occurring consonants in Arabic, is likely due to their articulatory complexity, which involves a secondary co-articulation (the so-called pharyngealization). The current findings help clarify the developmental trajectory of children’s acquisition of emphatic consonants and should be informative for both researchers and clinicians.

Extreme stop allophony in Mixtec spontaneous speech: Data, word prosody, and modelling

Word-level prosody plays an important role in processes of consonant lenition. Typically, consonants in word-initial position are strengthened while those in word-medial position are lenited (Keating, Cho, Fougeron, & Hsu, 2003). In this paper we examine the relationship between word-prosodic position and obstruent lenition in a spontaneous speech corpus of Yoloxóchitl Mixtec, an endangered Mixtecan language spoken in Mexico. The language exhibits a surprising amount of lenition in the realization of otherwise voiceless unaspirated stops and voiceless fricatives in careful speech. In Experiment 1, we examine the relationships between word position, consonant duration, and passive voicing and find that word-medial pre-tonic position is the locus of both consonant lengthening and less passive voicing. Non-pre-tonic consonants are produced with more voicing and shorter duration. We also find that the functional status of the morpheme plays a role in voicing lenition. In Experiment 2, we examine manner lenition and find a similar pattern – word-medial pre-tonic stops are more often realized with complete closure relative to non-pre-tonic stops, which are more often realized with incomplete closure. In Experiment 3, we model these lenition patterns using a series of deep neural networks and find that, even with limited training data, we can achieve reasonably high accuracy in the automatic categorization of lenition patterns. The results of this research both complement recent work on the phonetics of lenition in the world’s languages (Katz and Fricke, 2018; White et al., 2020) and provide computational tools for modeling and predicting patterns of extreme lenition.

Semantic Relation Extraction from Cultural Heritage Archives

Digital preservation technologies are now being increasingly adopted by cultural heritage organizations. This cultural heritage data is often disseminated in the form of digital text through a variety of channels such as Wikipedia, cultural heritage archives, etc. To acquire knowledge from digital data, the extraction technique becomes an important part. However, in the case of digital text, which has characteristics such as ambiguity, complex grammar structures such as the Thai language, and others, it makes it more challenging to extract information with a high level of accuracy. We thus propose a method for improving the performance of data extraction techniques based on word features, multiple instance learning, and unseen word mapping. Word features are used to improve the quality of word definition by concatenating parts of speech (POS) and word position is used to establish the accurate definition of a word and convert all of this into a vector. In addition, we use multiple instance learning to solve issues where words do not fully express the meaning of the triple. We also cluster the particular word to find the predicate word by removing words that are irrelevant between the subject and the object. The difficulty of having a new set of words that have never been trained before can be overcome by using unseen word mapping with sub-word and nearest neighbor word mapping. We conducted several experiments on a cultural heritage knowledge graph to show the efficacy of the proposed method. The results demonstrated that our proposed technique outperforms existing models currently utilized in relation to extraction systems. It can achieve excellent accuracy since its precision, recall, and F1 score are 0.89, 0.88, and 0.89, respectively. Furthermore, it also performed well in terms of unseen word prediction, precision, recall, and F1 score, which were 0.81, 0.87, and 0.84, respectively.

Geometry Attention Transformer with position-aware LSTMs for image captioning

In recent years, Transformer structures have been widely applied in image captioning with impressive performance. However, previous works often neglect the geometry and position relations of different visual objects. These relations are often thought of as crucial information for good captioning results. Aiming to further promote the image captioning by Transformers, this paper proposes an improved Geometry Attention Transformer (GAT) framework. In order to obtain geometric representation ability, two novel geometry-aware architectures are designed respectively for the encoder and decoder in our GAT by i) a geometry gate-controlled self-attention refiner, and ii) a group of position-LSTMs. The first one explicitly incorporates relative spatial information into the image representations in encoding steps, and the second one precisely informs the decoder of relative word positions for generating caption texts. The image representations and spatial information are extracted by a pretrained Faster-RCNN network. Our ablation study has proved that these two designed optimization modules could efficiently improve the performance of image captioning. The experiment comparisons on the datasets MS COCO and Flickr30K, also show that our GAT could often outperform current state-of-the-art image captioning models.

Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions

With the rapid development of educational informationization, Internet of Things, and other technologies, English education has been paid special attention to, and all aspects such as educational model, learning behavior, teaching philosophy, and teaching evaluation have been greatly influenced by educational informationization. Based on the experience of practical education, this paper explores and studies the connotation and characteristics of English test questions and its influence and application on modern English test questions. This paper constructs a systematic method to extract keywords from English test questions. It perfects the fair and reasonable index of English keywords, establishes the weight system, discusses the relationship of keywords, and conducts academic research on vocabulary, word frequency and word position, emphatically adopts BayesNet algorithm to extract keywords, and realizes the evaluation system of English test keywords based on intelligent analysis and weight relationship. The results show that (1) selecting the calculation method and weight relationship suitable for the text system to carry out intelligent analysis, the weight ratio exceeds 65%; that is, the text keyword retrieval is successful. (2) The average accuracy (%), average recall (%) and average F -measure (%) in weighted names are almost less than 70%. Only the BayesNet algorithm has 72.3% weight analysis in keyword extraction in reading comprehension. (3) KEA algorithm, PAT TERR algorithm, and BayesNet algorithm take 0-2.8 s, 0-2.6 s, and 0-2.1 s, respectively, and the BayesNet algorithm takes the shortest time. The calculation time of users is greatly saved. (4) According to the calculation results of CPT model, the sum of the weights of the three algorithms is equal to 1, and the BayesNet algorithm is dominant in extracting keywords with a weight analysis of 0.529 in verb translation.

Perception and expression of talker gender at the beginning and end of words

Among the common acoustic differences between the voices of women and men are the spectral characteristics of fricatives. Akin to pitch and formant frequencies, the frequency characteristics of fricatives are lower in men than in women. These fricative characteristics are learned social cues that help to express the talker’s gender. Listeners reliably incorporate these differences into perceptual judgments by categorizing the same sound differently depending on what they perceive the gender of the talker to be; a fricative that is intermediate to “s” and “sh” will be labeled “s” when thought to be spoken by a man, but “sh” if by a woman. However, the acoustic descriptions and the perceptual consequences of these gender-related acoustic differences have been explored almost exclusively at syllable-onset position. Here we use both word-onset and -offset positions in both perception and production tasks. The perceptual accommodation of talker gender is weaker when the fricative is at syllable offset, inviting questions about whether the extent of gender-related changes in acoustics are simply commensurate with salience of word position. We will present both the perception and production data in an attempt to unify both phenomena in relation to each other.

Nasalisation as a Phonological Process in Tiv

Tiv phonology has major and minor phonological processes and nasalisation belongs to the former. This study explores the nature of the Tiv nasalisation process. It describes Tiv nasals; discusses syllabic nasal consonants; examines the nature of nasalised vowels and differentiates between oral and nasal sounds. This study used Generative Phonology model which was popularised by Chomsky and Halle’s (1968). The Sound Pattern of English. This model formulates theories that help in the analysis of Tiv nasals and points out that vowels that precede nasal consonants are nasalised. The author used participant-observation instrument for data collection. Both primary and documentary sources are used in this study. It has been observed that Tiv phonology has pre-nasalised consonants. It has been discovered that Tiv language has five nasal consonants: /m/, /n/, /?/, /?/ and /?/. It has also been noted that /m/, /n/, /?/ and /?/ appear at the word-initial, medial and final positions of Tiv words. It has also been known that nasals have phonological, morphological and syntactic functions. It has been recommended that learners should pronounce n in nd, ng, nj, nk, and nz as /?d/, /?g/, /??/, /?k/ and /?z/ and should note that /m/ and /?/ are not the same: /m/ is a bilabial nasal whereas /?/ is labio-dental sound. Researchers should carry out acoustic description of nasals and describe articulatory processes of producing nasals.

Novel target attention convolutional neural network for relation classification

Relation classification (RC) is an essential task in natural language processing (NLP), which extracts relationships of entity pairs in sentences of text. In the paper, a novel target attention convolutional neural network (TACNN) is proposed for the RC by fully utilizing word embedding information and position embedding information. Simultaneously, a target attention mechanism (TAM) is applied into a context layer of the convolutional neural network (CNN) model, which increases the effect of the relationship matrix weights of two entities in the sentence, while ignoring the calculation of irrelevant terms. And the TACNN is essentially to modify the weight of the relationship matrix of entities in the sentence at the context layer and connect the relationship feature composed of the lexical layer feature with the target attention layer feature. Therefore, the TACNN simplifies the structure of the CNN and improves the computational efficiency. On SemEval-2010 Task 8 dataset and Conll04 dataset, the TACNN obtains 85.3% and 71.4% of the F1-score, respectively. In contrast to previously available public models, the TACNN achieves a state-of-the-art level in the F1-score of the RC.

Intentional remembering and intentional forgetting in working and long-term memory.

We show that the intention to remember information substantially improves recall of that information when maintained in working memory (WM), whereas it does not have the same effect on maintenance in episodic long-term memory (LTM). In eight experiments, participants processed lists of words according to a semantic orienting task in three instruction conditions: to remember, to forget, or an incidental-memory baseline with no instruction. The first two experiments showed better memory for intentional remembering compared to incidental memory for a typical test of WM, but not for a typical test of episodic LTM. The subsequent six experiments determined which of three variables distinguishing typical WM and episodic-LTM tests-delay of test, list length, and proactive interference-are responsible for this difference. The intention to remember improved free-recall performance for short lists (close to the capacity limit of WM), most strongly when tested immediately, and only in the presence of proactive interference. This result supports a functional distinction between WM and episodic LTM: Whereas episodic LTM keeps a nonselective record of experiences for future use, WM holds selectively only the information relevant for the current goal. In addition, we found a beneficial effect of intentional remembering on memory for the list position of words, which was obtained for longer lists and regardless of the delay of testing, probably reflecting episodic LTM. The instruction to forget had no effect compared to the incidental baseline, questioning the assumption of a targeted forgetting process on memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Spanish in Albuquerque, New Mexico: Spanish-English Bilingual Adults’ and Children’s Vocalic Realizations

This study explores vocalic production and variation in 29 Spanish-English bilingual children and adults from Albuquerque, New Mexico. Linear mixed-effects models analyzed the effects of lexical stress, word position, phonetic context, Spanish use, and lexical frequency on the F1 (height) and F2 (frontedness) values of 2041 /i e a o u/ vowels. Importantly, results show that /u/ fronting is pervasive in both children and adults’ speech, but in contrast to adults’ more general /u/ fronting, children’s Spanish use also predicted atonic /u/ fronting. Expanding the range of data to include children also showed that children’s realizations displayed a generalized stress effect, whereby the atonic space was condensed compared to the tonic space. The generalized stress effect was absent among the adults. Changes in the degree of phonetic convergence between the adults and children are attributed to acquisitional paths and demographic changes in their community.