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This study began with a simple question that has been on our minds, indMdually and collectively, for some time: What are the basic units of talk-in-conversation? Units in conversation must be understood as usable for the construction of joint activities, not merely as packages of information to be parsed. Features of turn construction are adapted to such functions as displaying responsiveness to other turns and making interpretable contributions to an ongoing interactional sequence. Furthermore, timing of speaker onset is crucial to the making of meaning in conversation, whether that onset is produced in overlap, after some gap, or precisely at the point where a current speaker stops. It has been manifestly clear for some time that, for the description of the basic units of talk-in-interaction, neither a strictly syntactic/semantic nor a strictly prosodic approach to conversational units will suffice by itself (Schegloff 1996); "sentences" and "clauses" are only part of the picture, but "tone units" (Crystal 7969; Cruttenden 1986) or "intonation units" (Chafe 1987, 1988, 1992, 7993, 1994; Du Bois et al. 1993) are also only part of the picture. The basic conversational unit must be an amalgam of at least these two types of units, but its niche in a developing interactional context must also play a major part in its construction. An obvious place to turn for discussion of this question is to Conversation Analysis (CA), since this framework has concerned itself intensively with the organization of turn-taking. In their celebrated and highly influential paper outlining the practices under$ing conversational turn-taking, Sacks et al. (1974) propose a model in which turns at talk are analyzed as being made up of "turn-constructional

Grammars can serve as producers for structured test inputs that are syntactically correct by construction. A probabilistic grammar assigns probabilities to individual productions, thus controlling the distribution of input elements. Using the grammars as input parsers, we show how to learn input distributions from input samples, allowing to create inputs that are similar to the sample; by inverting the probabilities, we can create inputs that are dissimilar to the sample. This allows for three test generation strategies: 1) Common inputs by learning from common inputs, we can create inputs that are similar to the sample; this is useful for regression testing. 2) Uncommon inputs learning from common inputs and inverting probabilities yields inputs that are strongly dissimilar to the sample; this is useful for completing a test suite with inputs from hell that test uncommon features, yet are syntactically valid. 3) Failure-inducing inputs learning from inputs that caused failures in the past gives us inputs that share similar features and thus also have a high chance of triggering bugs; this is useful for testing the completeness of fixes. Our evaluation on three common input formats (JSON, JavaScript, CSS) shows the effectiveness of these approaches. Results show that common inputs reproduced 96% of the methods induced by the samples. In contrast, for almost all subjects (95%), the uncommon inputs covered significantly different methods from the samples. Learning from failure-inducing samples reproduced all exceptions (100%) triggered by the failure-inducing samples and discovered new exceptions not found in any of the samples learned from.

In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that rely on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations.

Facade image parsing is essential to the semantic understanding and 3-D reconstruction of urban scenes. Considering the occlusion and appearance ambiguity in single-view images and the easy acquisition of multiple views, in this letter, we propose a multiview enhanced deep architecture for facade parsing. The highlight of this architecture is a cross-view feature aggregation module that can learn to choose and fuse useful convolutional neural network (CNN) features from nearby views to enhance the representation of a target view. Benefitting from the multiview enhanced representation, the proposed architecture can better deal with the ambiguity and occlusion issues. Moreover, our cross-view feature aggregation module can be straightforwardly integrated into existing single-image parsing frameworks. Extensive comparison experiments and ablation studies are conducted to demonstrate the good performance of the proposed method and the validity and transportability of the cross-view feature aggregation module.

As databases make Corpus Linguistics a common tool for most linguists, corpus annotation becomes an increasingly important process. Corpus users do not need only raw data, but also annotated data, submitted to tagging or parsing processes through annotation protocols. One problem with corpus annotation lies in its reliability, that is, in the probability that its results can be replicable by independent researchers. Inter-annotation agreement (IAA) is the process which evaluates the probability that, applying the same protocol, different annotators reach similar results. To measure agreement, different statistical metrics are used. This study applies IAA for the first time to the Valencia Espaol Coloquial (Val.Es.Co.) discourse segmentation model, designed for segmenting and labelling spoken language into discourse units. Whereas most IAA studies merely label a set of in advance pre-defined units, this study applies IAA to the Val.Es.Co. protocol, which involves a more complex two-fold process: first, the speech continuum needs to be divided into units; second, the units have to be labelled. Kripendorffs u -family statistical metrics (Krippendorff et al. 2016) allow measuring IAA in both segmentation and labelling tasks. Three expert annotators segmented a spontaneous conversation into subacts, the minimal discursive unit of the Val.Es.Co. model, and labelled the resulting units according to a set of 10 subact categories. Kripendorffs u coefficients were applied in several rounds to elucidate whether the inclusion of a bigger number of categories and their distinction had an impact on the agreement results. The conclusions show high levels of IAA, especially in the annotation of procedural subact categories, where results reach coefficients over 0.8. This study validates the Val.Es.Co. model as an optimal method to fully analyze a conversation into pragmatically-based discourse units.

The TPR (Total Physical Response) method in its implementation through speech instructions given to learners can be played through real examples that are meaningful and understandable. The nature of this study is descriptive analysis, which is the regular parsing of data that has been obtained, then given understanding and explanation in order to be well understood by readers. So that the steps taken are 1) learners must actively listen well to what is demonstrated by the teacher with the vocabulary keywords mentioned and the commands given by the teacher with examples of the use of such vocabulary in everyday life or phenomena that are around, 2) the student's task in listening to the teacher's demonstration explanation explaining the existing vocabulary must be in some time and not just once and thoroughly, 3) The next stage is to deify and imitate, 4) the teacher tries to order students without keywords or demonstrations or other stimulus, 5) Students act or demonstrate teacher commands, 6) creative and innovative demonstrations are carried out by teachers so that students are more understanding and able to imitate well, 7) teachers give varied command models without demonstrations or demonstrations, and 8) then in the final stages students are able to protest to colleagues in turn.

Although the correlation between general cognitive ability (g) and performance on speeded cognitive tasks is well-established, there is need for a better understanding of how successive stages of processing contribute to this relationship. Previous research suggests that g is primarily associated with the rapidity of decision-making rather than perceptual processing of stimuli; the implication is that g should statistically interact with a manipulation affecting the difficulty of the decision process, while simultaneously failing to interact with a perceptual manipulation. We applied Sternberg's method of additive factors to test this hypothesis in two reaction time tasks, each of which systematically manipulated the demands on perceptual acuity and decision-making. With a total of 773 participants, we found evidence of an interaction between a short-form measure of g and the decisional-but not perceptual-manipulations. This pattern was found in both number-comparison (Experiment 1) and tone-comparison (Experiment 2) tasks. Additionally, diffusion modeling of the Experiment 1 results revealed that the diffusion rate (v) is associated with g and affected by an informational attribute of the stimulus (numerical magnitude) but not a perceptual attribute (contrast); the nondecision time (Ter) is not associated with g and shows the opposite pattern of selective influence. Taken together, these findings add to the evidence for a theoretical framework partitioning reaction time into several processing stages, of which only the decision-making stage is associated with g. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

O uso de ferramentas de ensino tornou-se uma alternativa para complementar a aprendizagem do conteúdo escolar. Este artigo faz uma panorâmica de aspectos da Compilação e análise de desempenho das ferramentas computacionais GALS, Grammophone, The Context Grammar Free Checker, Verto e Parsing Simulator que foram desenvolvidas para suporte ao processo de compilação e têm como objetivo o apoio ao aprendizado da disciplina de Compiladores. Há várias ferramentas conhecidas, porém só algumas foram construídas para fins acadêmicos e serão apresentadas neste trabalho, bem como foram testadas com alunos na disciplina de Compiladores do curso de Graduação e também no Mestrado em Ciência da Computação de uma Universidade Pública Brasileira no Paraná para analisar hipóteses, auxiliar na verificação de exemplos de parsing e trocar experiências sobre essas ferramentas de Compilação. As fases de análise léxica e principalmente sintática tornaram-se mais didáticas e atraentes aos alunos, ficando fácil de entender suas funcionalidades e implementação de um compilador como um todo. GALS se mostrou ser uma boa opção com uma simples interface, trabalhando análise léxica e sintática para mais de uma linguagem (Java, C++ e Delphi). Estudos de Gramáticas Livres de Contexto no formato LL(1), LR(0) e LR(1) podem ser favorecidos não só com GALS, mas também com as ferramentas Grammophone e The Context Grammar Free Checker. Já o Verto trabalha de forma didática não só as etapas de análise léxica e sintática (essa também com Parser LR(1)), mas também a geração de códigos. Parsing Simulator se mostrou uma ferramenta intuitiva e também apresenta um acervo extenso de opções de análise sintática exibindo o passo a passo das tabelas de análise LL(1) e LR(K) promovendo o ensino-aprendizagem em Compiladores.

Institutions—defined as strategies, norms and rules (Ostrom Understanding institutional diversity, Princeton University Press, Princeton, 2005)—are omnipresent in third sector contexts. In this paper, we present the Institutional Grammar (IG) as a theoretically informed approach to support institutional analysis in third sector research. More specifically, the IG coding syntax allows the researcher to systematically wade through rich text and (transcribed) spoken language to identify and dissect institutional statements into finer syntactical segments of interest to the researcher. It is a versatile method that can generate data for small- or large-N research projects and can be integrated with mixed-method research designs. After first introducing and describing the IG, we present a case study to illustrate how a IG-based syntactic analysis can be leveraged to inform third sector research. In the case, we ask: Do the rules embedded in regulatory text addressing the involuntary dissolution of charity organizations differ between bifurcated and unitary jurisdictions in the United States? Using IG’s ABDICO 2.0 syntax, we identify eleven “Activation Condition” (AC) categories that trigger action and assess variation among the 46 jurisdictions. We ultimately conclude that the rules do not differ between bifurcated and unitary jurisdictions, but that finding is not the primary concern. The case demonstrates IG as an important methodological advance that yields granular, structured analyses of rules, norms and strategies in third sector settings that may be difficult to identify with other methods. We then emphasize four areas of third sector research that could benefit from the addition of IG-based methods: analysis of (1) rule compliance, (2) inter-organizational collaboration, (3) comparative study of institutional design, and (4) the study of institutional change. We close the paper with some reflections on where IG-based analysis is headed.

Linguistical emphasis is not being thoroughly discussed in syntax books، except in pieces and partitions، initially with its verbal or semantic description، to its study of using the tool، extra letters، or sentences and ending with methods such as the division، exclusion، advance and delay، and so on due to its importance that calls for it to be in multiple forms، and miscellaneous images. Based on that، we looked at the divisions of the emphasis and its terminology، and found that some emphasis naturally expresses an emphasis and is parsed as an emphasis، and some emphasis naturally expresses an emphasis but is not parsed as one، or from its meaning it is understood to be an emphasis but is not also parsed as one، as well as those which strengthen the emphasis. We also noted the multiplicity of its terminology among the Arab linguists due to viewing it from different perspectives.

The purpose of the article is the need to develop a concept for the formation of communication using parser programs in the personnel department of higher education to improve educational activities. Methodology. In the process of research the methods of informology and social communication, logical-dialectical method of cognition, methods of system analysis, abstractions were applied, which allowed to reveal the content of implementation of parser programs; to analyze the use of parser programs in higher education institutions on the example of Vasyl' Stus Donetsk National University. Scientific Novelty. The necessity of introduction and use of parser programs in higher education institutions is substantiated. The use of Google Spreadsheet in the human resources department of the university as an information retrieval system and text-digital database to improve the educational activities of higher education institutions has been studied. Conclusions. Analyzing the communications of the institution of higher education, it is necessary to name their main directions: communication between the organization and its external environment; communication between management levels and departments; communication manager-subordinate; communication between the leader and the working group; informal communications. The purpose of using parser programs in higher education institutions is primarily to increase the efficiency of the educational process, improve service and reduce costs in the data processing. The use of parsing in the personnel department of a higher education institution is multifaceted. Today, parser programs perform parsing, lexical, automated analysis, process many information requests, parse documents, extracting from there the necessary and useful user data. The Google Spreadsheets parser program aims to use the latest information and communication technologies that help improve management, improve employee interaction, and promote social innovation.

In Modern Hebrew, prefixoids such as tat ‘sub’, χad ‘mono’, etc. appear on nouns and suffixed adjectives. The status of the prefixoid is not clear: in some respects, it behaves like the morphological head, but in others it behaves like the dependent. Focusing on tat ‘sub’, this paper provides both the first thorough description of such an item and a morphological analysis. It is claimed that the prefixoid is always a nominal modifier. In some configurations, the modified nominal structure is null. These claims are shown to account for the selectional restrictions of the prefixoid, as well as the agreement and inflection patterns of the different items with tat.

The intelligent code search with natural language queries has become an important researching area in software engineering. In this paper, we propose a novel deep learning framework At-CodeSM for source code search. The powerful code encoder in At-CodeSM, which is implemented with an abstract syntax tree parsing algorithm (Tree-LSTM) and token-level encoders, maintains both the lexical and structural features of source code in the process of code vectorizing. Both the representative and discriminative models are implemented with deep neural networks. Our experiments on the CodeSearchNet dataset show that At-CodeSM yields better performance in the task of intelligent code searching than previous approaches.

Artikel ini beranjak dari kondisi beberapa gereja yang ini sangat memprihatinkan. Tidak sedikit gereja yang hidup segan, mati tak mau, Bahkan ada gereja yang sepi kegiatan, kalaupun ada sifatnya hanya ritinitas, apalagi di masa pandemic covid -19 seperti sekarang ini, khususnya gereja-gereja kecil di perkampungan. Berdianm diri, menunggu uluran tangan dari para dermawan. Oleh karena itu artikel ini akan membahas tentang pentingnya peran seorang pemimpin Gereja sebagai pelaku perubahan bagi kemaat yang digembalakannya. Penelitian ini menggunakan metode pustaka dengan pendekatan kualitatif deskriptif. Penekanannya adalah pada kajian terhadap sumber-sumber pustaka dan mengurainya serta memberi penjelasan dalam sebuah kerangka uraian. Analisis sumber utama adalah literasi pustaka yang mengkaji tentang pemimpin Gereja yang visioner sebagai pelaku perubahan yang dilengkapi oleh berbagai artikel jurnal dan sumber akademik lainnya yang relevan dengan topik yang dibahas. Visi adalah hal penting yang harus dimiliki oleh seorang pemimpin gereja, sebab visi akan memberikan arah yang jelas. Visi adalah salah satu factor penentu keberhasilan seorang pemimpin.

The identification of interest/disinterest over a notion is having a huge demand in the current competitive data analytical world. For example, the customer preferences in various seasons, approximate visitors to a tourist place based on scenarios like weather and special occasions in the place, and so on. While giving an opinion on any concept, natural language in form of sentences/words/symbols/ratings plays a vital role. Depends upon the context and usage of natural language, captured opinions can be interpreted as either in a positive or negative sense. The terminology used for providing the opinions is used for analysing the data in an easy way. The evaluation of the word frequencies and word cloud are identified accurately, only after a keen analysis of the collected opinions. The Term-Document Matrix is one of the techniques that identify the frequency of words in each and every document/row in the given dataset, which can be used to generate the word cloud. In this paper to identify the frequency of words from the opinions given by multi-domain personalities on Astrology, distinct Natural Language Processing (NLP) techniques are used. A word cloud can also be generated from the set of words used for the astrological dataset.

Recently, IT specialties have become one of the most demanded specialties in the world labor market. Simultaneously the traditional teaching in conditions of mass production, even with a professional teacher, has a significant drawback – the fundamental impossibility of adapting to each student. Since the 60s of the twentieth century, researchers worldwide have been developing various computer-tutoring tools that have, less or more, adaptive functions. Nevertheless, the task of the perfect computer tutor development is still far from being solved. The article's research subject is the process of student requests analyzing during intelligent computer tutoring in SQL. The main goal is to develop a method for analyzing the student's SQL queries. The purposes: to form a general scheme and features of the method for analyzing student’s SQL queries based on the principles of technical diagnostics and methods of lexical, syntactic analysis of computer programs; to develop methods for parse tree construction; to create methods for comparing reference and real SQL queries according to their similarity rate; to demonstrate the function ability of the developed methods on specific examples. The methods used computer programs the automatic testing method, the computer programs lexical and syntactic analysis methods, the computer programs parsing trees construction methods, the objects diagnosing method based on comparison with a reference, the strings analysis methods, the method of q-grams. The following results were obtained: the student’s SQL queries analysis method was formed based on a system approach including automatic testing on real data, building query-parsing trees, comparison with a reference, and comprehensive determination of the queries similarity rate. The scientific novelty is the improvement of the method for the student's SQL query analysis during intelligent computer training in SQL query composition.

A critical question in the study of language development is to understand lexical and syntactic acquisition, which play different roles in speech to the extent it would be natural to surmise they are acquired differently. As measured through the comprehension and production of closed-class words, syntactic ability emerges at roughly the 400-word mark. However, a significant proportion of the developmental work uses a coarse combination of function and content words on the MacArthur-Bates Communicative Development Inventory (MB-CDI). Using the MB-CDI Wordbank database, we implemented a factor analytic approach to distinguish between lexical and syntactic development from the Words and Sentences (WS) form that involves both function words and the explicit categorizations. Although the Words and Gestures (WG) form did not share the factor structure, common WG/WS elements recapitulate the expected age-related changes. This parsing of the MB-CDI may prove simple, yet fruitful in subsequent investigation.

This article analyzes the function of imperative sentences contained in the "The Captain" movie by applying Zhang's theory of imperative sentence function. The research method used is descriptive qualitative research methods. The data source in this research is the 2019 China movie“The Captain” (中国机长 Zhōngguó Jīzhǎng). The data are imperative words and sentences in the movie. The data collection technique used was the observation and note technique according to Mahsun, then used the data analysis technique of Miles and Huberman. Researchers found 281 imperative sentences in "The Captain" movie and analyze the imperative function. The most widely used imperative function is the commanding function, which is 80 sentences. Meanwhile, the least function used was a threatening function with only 1 sentence. The commanding function is more dominant than other functions and is found in the conversations between officers to passengers, flight attendants to passengers, communication between officers and others. The commanding function is also mostly found in the officer’s conversations, especially when they were facing an emergency situation. Meanwhile, the words used by the officers, flight attendants, and the captain were showing respect and courtesy. So that the threatening function was less found in this movie. There are no requesting and begging functions because this movie tells about the incident of the Sichuan flight 8633, including the service of the aircraft officers and the confidence of the officers in facing the emergency situation at that time.

One perennially important question for theories of sentence comprehension is whether the human sentence processing mechanism is parallel (i.e., it simultaneously represents multiple syntactic analyses of linguistic input) or serial (i.e., it constructs only a single analysis at a time). Despite its centrality, this question has proven difficult to address for both theoretical and methodological reasons (Gibson & Pearlmutter, 2000; Lewis, 2000). In the present study, we reassess this question from a novel perspective. We investigated the well-known ambiguity advantage effect (Traxler, Pickering, & Clifton, 1998) in a speeded acceptability judgment task. We adopted a signal detection theoretic approach to these data, with the goal of determining whether speeded judgment responses were conditioned on one or multiple syntactic analyses. To link these results to incremental parsing models, we developed formal models to quantitatively evaluate how serial and parallel parsing models should impact perceived sentence acceptability in our task. Our results suggest that speeded acceptability judgments are jointly conditioned on multiple parses of the input, a finding that is overall more consistent with parallel parsing models than serial models. Our study thus provides a new, psychophysical argument for coactive parses during language comprehension. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Temporarily ambiguous sentences that are disambiguated in favor of a less preferred parse are read more slowly than their unambiguous counterparts. This slowdown is referred to as a garden path effect. Recent self-paced reading studies have found that this effect decreased over the course of the experiment as participants were exposed to such syntactically ambiguous sentences. This decrease in the magnitude of the effect has been interpreted as evidence that readers calibrate their expectations to the context; this minimizes their surprise when they encounter these initially unexpected syntactic structures. Such recalibration of syntactic expectations, referred to as syntactic adaptation, is only one possible explanation for the decrease in garden path effect, however; this decrease could also be driven instead by increased familiarity with the self-paced reading paradigm (task adaptation). The goal of this paper is to adjudicate between these two explanations. In a large between-group study (n = 642), we find evidence for syntactic adaptation over and above task adaptation. The magnitude of syntactic adaptation compared to task adaptation is very small, however. Power analyses show that a large number of participants is required to detect, with adequate power, syntactic adaptation in future between-subject self-paced reading studies. This issue is exacerbated in experiments designed to detect modulations of the basic syntactic adaptation effect; such experiments are likely to be underpowered even with more than 1200 participants. We conclude that while, contrary to recent suggestions, syntactic adaptation can be detected using self-paced reading, this paradigm is not very effective for studying this phenomenon. © 2021, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1037/xlm0001046

This study aims to: (i) analyze the tree diagram structure of sentence patterns found in the data source, (ii) analyze the types of translation shifts of sentence patterns in the translation process from source language into target language. The novel entitled “Buddha” is a non-fiction book by Karen Armstrong. It tells about an examination of the life, times, and lasting influence of Siddharta Gautama with core tenets of Buddhism introduced throughout history. This research focuses on The Syntax Analysis and Its Translation Found on Sentence Patterns in the Novel entitled “Buddha”. This study aims at analyzing the tree diagram structure and the types of translation shifts found in the novel entitled “Buddha”. The analysis uses the theory of sentence patterns from Quirk and Greenbaum and the theory of translations shift proposed by Catford. The process of collecting data was started by reading the entire data source to understand the story in the novel entitled “Buddha” and observe the data of sentence patterns that can be taken from the story entitled “Buddha”. In the method of collecting data, the data source was read to find out complex sentences and simple sentence found in the story. The finding of the research is that there are seven sentence patterns that are translated by applying unit shift. The researcher uses the theories to support this research. Those are syntax, types of sentences, sentence pattern, tree diagram, and relevant previous studies. Syntax is stated as one of the branches that focus on the sentence structure. This research study uses theory of translation shift proposed by Catford

Pāṇini’s grammar is an important milestone in the Indian grammatical tradition. Unlike grammars of other languages, it is almost exhaustive and together with the theories of śābdabodha (verbal cognition), this grammar provides a system for language analysis as well as generation. The theories of śābdabodha describe three conditions necessary for verbal cognition. They are ākāṅkṣā (expectancy), yogyatā (meaning congruity), and sannidhi (proximity). We examine them from a computational viewpoint and provide appropriate computational models for their representation. Next, we describe the design of a parser following the theories of śābdabodha and present three algorithms for solving the constraints imposed by the theories of śābdabodha . The first algorithm is modeled as a constraint satisfaction problem, the second one as a vertex-centric graph traversal, and the third one as an edge-centric binary join, each one being an improvement over the previous one.

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results.

In this paper, we present ParVis, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input. We used our system to visualize the behavior of textual as well as visual parsers and describe here two of these uses. Moreover, in order to validate the efficacy of our system, we ran a user experiment where students analyzed a CUP-generated parser both with ParVis, instantiated to LALR parsers, and the standard CUP debug facilities. The results show that students can indeed analyze parser behavior and find mistakes in parser specifications more easily and quickly using ParVis. In particular, in some parser design tasks, using ParVis participants achieved a higher success rate of 50% in 42% less time with respect to the baseline system.

Human parsing, especially in the wild, has attracted a lot of attention due to its great potential in many real-world applications. The Pyramid Spatial Parsing (PSP) module has shown superior performances in scene and human parsing tasks. However, the basic AvgPool operation in PSP equally aggregates spatial clues of a local region, and thus mixes up influences of different human parts presented in this region. It results in failures in capturing useful contexts relevant to parsing different parts. To address this problem, a suitable mechanism to collect spatial clues aligning with different human parts is proposed in this paper. We employ a Gather-Excite (GE) operation, a replacement of the AvgPool-Upsample operation in a pyramidical structure, to accurately reflect relevant human parts of various scales. The GE operation contains two steps: the gather operation that adaptively aggregates spatial clues to relevant human parts, and the excite operation that generates new feature maps with the gathered contextual information. This results in a novel Pyramidical Gather-Excite Context (PGEC) module to solve the multi-scale problem and parse person at various scales. The PGEC module is composed of multiple GE operations with different spatial extents and aggregates local and global spatial clues for better modeling multi-scale contextual information in parallel. Moreover, we integrate the PGEC module with fine-grained details, edge preserving module and deep supervision to formulate a novel PGEC Network (PGECNet) for human parsing. The proposed PGECNet has achieved state-of-the-art performance on four single-person human parsing datasets ( i.e. , LIP, PPSS, ATR and Fashion Clothing) and two multi-person human parsing datasets ( i.e. , PASCAL-Person-Part and CIHP). The experimental results show that the proposed PGEC is superior to the PSP and ASPP modules especially in single-human parsing task. The source code is publicly available at https://github.com/31sy/PGECNet .

For almost two decades, phonologists, phoneticians and psycholinguists have devoted attention to the value of prosodic information during silent reading. Until then information on the influence of syntactic and semantic aspects in silent reading was the focus of attention in Psycholinguistics. Despite the joint efforts of researchers, there are many issues to be explored regarding two main domains. The first relates to individual prosodic parameters to languages that can have influence on the processing of sentences. The second refers to how the parser uses the prosodic information present in written stimulus on the understanding of silently reading. In both situations, it will be necessary to focus on methodological aspects in addition to theoretical ones. If on the one hand we already have strong evidence about the influence of prosody in silent reading, on the other hand it cannot be denied that measuring the prosody mentally organized by the reader during reading process is not an easy task. Thus, this article intends to emphasize the complexity concerning the theme and at the same time the needs for further investigation in each language alone. It presents some insights from the early researches in Portuguese language and offers some suggestions for future investigation.

The lyrics generator should consider the context and the singability of the songs because every song expresses a story through the context of lyrics, and the lyrics should sound with the music well. Therefore, this study proposes a framework to generate the singable lyrics, and the context of lyrics should fit the given musical style. For the context, this study adopts the GPT-2 model which is powerful for text generation. The conditional GPT-2 model can be used to generate lyrics according to the given style. For suitable for singing, this study adjusts the structure and rhyme of lyrics through the use of a syntactic parser and a rhyme modification module. With automatic and human evaluations, the experimental results show that the proposed method can generate lyrics with high structural consistency, rhyme consistency, and originality according to the given music style.

This article argues that policymakers’ individual attributes influence their willingness to engage in policy innovation, and that this influence is responsive to, but not determined by, changes in the institutional structure. We derive these findings by employing principal component analysis of original data from surveys of local policymakers in China, to inductively locate different personalities. We find statistically significant personalities that influence a willingness to innovate, and that this influence is responsive to changes such as heightened risk. In addition to parsing the influence of extrinsic and intrinsic motivations on policy innovation, we further find that the traditional risk-acceptant policy-entrepreneur personality does not explain innovation well.

In the context of composing Arabic sentences, the rules of kaifiatul ikhbar are included in the rules which are basic and foundation. The kaifiatul ikhbar rules are formed from the composition of the mubtada and khobar and are used in various forms of sentences, including the sentence structure of the marriage consent. Accuracy in pronouncing the kabul marriage license sentence including the arrangement of the preacher and khobar becomes important considering this kabul consent will be a legal requirement or cancellation of a marriage contract. This study attempts to analyze and present several forms of kabul mariage agreement and syntactic analysis, especially in the kaida of kaifiatul ikhbar. By using a library approach and linguistic analysis from its syntactic aspects, the results of this study indicate that there are 11 forms of kaifiyatul ikhbar with various syntactic analysis specifications. This shows that the form of kaifiatul ikhbar in the marriage contract is actually not only one forms, but with a variety of sentence forms

Like those birds born to chirp, humans are born to parse; children are predisposed to assign linguistic structures to the amorphous externalization of the thoughts that we encounter. This yields a view of variable properties quite different from one based on parameters defined at Universal Grammar (UG). Our approach to language acquisition makes two contributions to Minimalist thinking. First, in accordance with general Minimalist goals, we minimize the pre-wired components of internal languages, dispensing with three separate, central entities: parameters, an evaluation metric for rating the generative capacity of grammars, and any independent parsing mechanism. Instead, children use their internal grammar to parse the ambient external language they experience. UG is “open,” consistent with what children learn through parsing. Second, our understanding of language acquisition yields a new view of variable properties, properties that occur only in certain languages. Under this open UG vision, specific elements of I-languages arise in response to new parses. Both external and internal languages play crucial, interacting roles: unstructured, amorphous external language is parsed and a structured internal language system results. My Born to parse (Lightfoot 2020) explores case studies that show innovative parses of external language shaping the history of languages. I discuss 1) how children learn through parsing, 2) the role of parsing at the two interfaces between syntactic structure and the externalization system (sound or sign) and logical form, 3) language change, and 4) variable linguistic properties seen through the lens of an open UG. This, in turn, yields a view of variable properties akin to that of evolutionary biologists working on Darwin’s finches; see section 7.

The rapid proliferation of artificial intelligence has led to the development of sophisticated cutting-edge systems in natural language processing and computational linguistics domains. These systems heavily rely on high-quality dataset/corpora for the training of deep-learning algorithms to develop precise models. The preparation of a high-quality gold standard corpus for natural language processing on a large scale is a challenging task due to the need of huge computational resources, accurate language identification models, and precise content parsing tools. This task is further exacerbated in case of regional languages due to the scarcity of web content. In this article, we propose a generic framework of Corpus Analyzer - Corpulyzer - a novel framework for building low resource language corpora. Our framework consists of corpus generation and corpus analyzer module. We demonstrate the efficacy of our framework by creating a high-quality large scale corpus for the Urdu language as a case study. Leveraging dataset from Common Crawl Corpus (CCC), first, we prepare a list of seed URLs by filtering the Urdu language webpages. Next, we use Corpulyzer to crawl the World-Wide-Web (WWW) over a period of four years (2016-2020). We build Urdu web corpus “UrduWeb20” that consists of 8.0 million Urdu webpages crawled from 6,590 websites. In addition, we propose Low-Resource Language (LRL) website scoring algorithm and content-size filter for language-focused crawling to achieve optimal use of computational resources. Moreover, we analyze UrduWeb20 using variety of traditional metrics such as web-traffic-rank, URL depth, duplicate documents, and vocabulary distribution along with our newly defined content-richness metrics. Furthermore, we compare different characteristics of our corpus with three datasets of CCC. In general, we observe that contrary to CCC that focuses on crawling the limited number of webpages from highly ranked Urdu websites, Corpulyzer performs an in-depth crawling of Urdu content-rich websites. Finally, we made available Corpulyzer framework for the research community for corpus building.

International audience; Some users try to post false reviews to promote or to devalue other's products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data.

A requirement specification for software is usually described in a natural language and thus may include sentences containing ambiguity and contradiction. Design errors due to ambiguous expressions and contradictions are often found later in the development process. These ambiguous sentences would force the developer to go back to the design process again. In order to prevent this kind of rework, a method of automatically converting a required specification written in Japanese to a state transition model is desired to help detect ambiguity and contradiction points of the specification. In previous studies, we have improved the extraction accuracy by refining the parsing rules. However, in order to parse a variety of sentences, it is necessary to set up new rules or refine the rules. In this paper, we propose a method of using a dependency analyzer in syntactic parsing. Using dependency analyzers eliminates the need for both of rule setting and refinement. Thus, the disadvantage would be eliminated. In addition, the effectiveness of the proposed method is reported by extracting and showing the elements necessary to create a state transition diagram.

Humans can understand a novel sentence by parsing it into known components like phrases and clauses. To achieve human-level artificial intelligence, compositional generalization tasks are suggested and used to assess machine learning models. Among those tasks, the SCAN tasks are challenging for the standard deep learning models, such as RNN sequence-to-sequence models and Transformers, that show great success across many natural language processing tasks. Even though a long line of deep learning research has developed memory augmented neural networks aimed at the SCAN tasks, their generalities remain questionable for more complex and realistic applications where the standard seq2seq models dominate. Hence, one needs to propose a method that helps the standard models to discover compositional rules. To this end, we propose a data augmentation technique using paring trees. Our technique annotates targets by inserting a new delimiter token in between them according to their parsing trees. For the training stage, the technique needs prior knowledge about the targets' semantic or syntactic compositionality. On the other hand, for the test stage, the technique uses no such knowledge. Experiments show that our technique enables the standard models to achieve compositional generalization on the SCAN tasks. Furthermore, we validate our technique on a synthetic task and confirm the standard models' strong performance gains without using prior knowledge about semantic compositionality. As one way to infuse parsing tree information into sequences, our technique can be used for tasks with structured targets like program code generation tasks.

Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. Despite the apparent ease of rapid categorization, it is a very computationally demanding task, given severely limited “bottlenecks” of attention and working memory capable of processing only a few objects at a time. Here, we tested whether this rapid categorical parsing is automatic or requires attention. We used the visual mismatch negativity (vMMN) ERP component known as a marker of automatic sensory discrimination. 20 volunteers (16 female, mean age—22.7) participated in our study. Loading participants’ attention with a central task, we observed a substantial vMMN response to unattended background changes of categories defined by certain length-orientation conjunctions. Importantly, this occurred in conditions where the distributions of these features had several peaks and, hence, supported categorical separation. These results suggest that spatially intermixed objects are parsed into distinct categories automatically and give new insight into how the visual system can bypass the severe processing restrictions and form rich perceptual experience.

Two major forces have contributed to the fast growth of human genetic data. One from medical research supported by governments and academic institutes; the other from direct-to-consumer (DTC) sequencing companies. While the former benefits from meticulously designed sequencing standards and quality control procedures, the latter comes in various formats and sequencing methods which are subject to changes over time and the particular needs of different companies. Thanks to the general public who shared their DNA data without constraint, here we provide a review for over 7000 genomes made public between 2011 and 2020, and produced by over six DTC sequencing companies. An open source tool-kit to systematically parse, quality check and filter genome files and statistically problematic alleles is provided to prepare consumer DNA datasets for research. The GenomePrep output is available in two common DNA datafile formats to enable further analysis with other tools. We also provide for download the combined output for all OpenSNP array genomes processed in this paper in a single data freeze file.

The prime objective of the proposed study is to determine the induction of Greibach Normal Form (GNF) in Arithmetic Expression Grammars to improve the processing speed of conventional LL(1) parser. Conventional arithmetic expression grammar and its equivalent LL(1) is used in the study which is converted. A transformation method is defined which converts the selected grammar into a Greibach normal form that is further converted into a GNF based parser through a method proposed in the study. These two parsers are analyzed by considering 399 cases of arithmetic expressions. During statistical analysis, the results are initially examined with the Kolmogorov-Smirnov and Shapiro-Wilk test. The statistical significance of the proposed method is evaluated with the Mann-Whitney U test. The study described that GNF based LL(1) parser for arithmetic take fewer steps than conventional LL(1) grammar. The ranks and asymptotic significance depict that the GNF based LL(1) method is significant than the conventional LL(1) approach. The study adds to the knowledge of parsers itself, parser expression grammars (PEG’s), LL(1) grammars, Greibach Normal Form (GNF) induced grammar structure, and the induction of Arithmetic PEG’s LL(1) to GNF based grammar.

The aim of this article is to parse the significance of the implementation of transformational leadership in Islamic educational institutions. Research libraries are the methods used in this research. The results of the parsing of the concept and theory of characteristics and components of transformational leadership are found that transformational leadership has positive implications for the various critical variables that contribute to the advancement of Islamic Educational institutions. For example, able to build awareness of vision together, build trust, constructing organizational citizenship behaviour, developing motivation, more empowerment, build self-efficacy, and learning organization. It can be on the theoretical basis that to improve the quality of Islamic education institutions hence the importation of transformational leadership is absolutely done.

Pandemic situations knocks at the door without information. In such situation healthcare is a major concern. Increased count of patients in any adverse condition leads to scarcity of healthcare services. In such situation telemedicine has played a very important role to overcome health problems of patient & to promote the remote healthcare monitoring of patients by physician in online mode. This proved to be very helpful for normal people but physically challenged could not take its advantage because of disabilities. We have implemented a system to reduce the communication barrier. System include many modules most importantly sign recognition & sign interpretation module in combination with ISL database of medical terms. For sign recognition we are using HMM model with Haar cascade classifier. Interpretation system fully uses the language technology including POS tagging, parsing etc. The overall system proved to improve the accuracy & efficiency of online consultation system for Deaf & Dumb people.

В статье описывается экспериментальное исследование метода разрешения синтаксической неоднозначности в конструкциях с сирконстантами с помощью онтологической семантики на основе универсального лингвистического процессора AIIRE (Artificial Intelligence Information Retrieval Engine). Выявлены четыре типа неоднозначных конструкций с сирконстантами, и составлены соответствующие поисковые запросы в Национальный корпус русского языка (НКРЯ). В результате получен список из 200 неоднозначных конструкций. Неоднозначность в конструкциях устраняется путем автоматического разбора и последующего ручного выбора его правильных вариантов. Однако на этом этапе возможны следующие проблемы: «разрывы» внутри конструкций, которые обозначают отсутствие нужных семантических связей внутри конструкции, а также большое количество вариантов синтаксического анализа, называемое комбинаторным взрывом. Эти проблемы решаются с помощью таких инструментов AIIRE, как Ontohelper и онтология. Онтология используется для обработки языковых данных и понимается как набор лексических значений или понятий и отношений между ними. Ontohelper – это вспомогательный инструмент с интерфейсом редактирования, где можно моделировать и задавать с помощью онтологическихотношенийвалентностиглаголов. В результате получаются корректные разборы для 66/200 конструкций, и обосновывается,чтоэффективностьданногометодазависитоткачестваиправильностимоделированияпонятийвонтологии.

In many sign languages around the world, some verbs can express grammatical agreement with not just one but two arguments, while other verbs do not express agreement at all. Moreover, and rather curiously, there is a remarkable degree of semantic overlap across sign languages between verbs that possess agreement properties. It has been suggested that iconicity has some part to play in this: in sign languages, there is the potential for aspects of verb meaning to be iconically represented in a verb’s form. In this dissertation, I investigate how semantics and morphosyntactic structure interact in constructions containing verbs with varying agreement properties in German Sign Language (DGS), using naturalistic dialogues between signers from the DGS Corpus as the primary data source. I show that certain semantic properties – also known to govern transitivity marking in spoken languages – are predictive of verb type in DGS, where indeed systematic iconic mappings play a mediating role. The results enable the formulation of cross-linguistic predictions about the interplay between verb semantics and verb type in sign languages. A subsequent analysis of a range of morphosyntactic properties of different verb types leads up to the conclusion that even ‘plain’ verbs, in fact, grammatically agree with their arguments. This in turn motivates a unified syntactic analysis in terms of agreement of constructions with verbs that do and do not overtly express it, thus presenting a novel solution to the typological puzzle that supposedly only verbs of a (partially) semantically definable subset agree in DGS and other sign languages.

Motivated by applications in robotics, we consider the task of synthesizing linear temporal logic (LTL) specifications based on examples and natural language descriptions. While LTL is a flexible, expressive, and unambiguous language to describe robotic tasks, it is often challenging for non-expert users. In this paper, we present an interactive method for synthesizing LTL specifications from a single example trace and a natural language description. The interaction is limited to showing a small number of behavioral examples to the user who decides whether or not they exhibit the original intent. Our approach generates candidate LTL specifications and distinguishing examples using an encoding into optimization modulo theories problems. Additionally, we use a grammar extension mechanism and a semantic parser to generalize synthesized specifications to parametric task descriptions for subsequent use. Our implementation in the tool LtlTalk starts with a domain-specific language that maps to a fragment of LTL and expands it through example-based user interactions, thus enabling natural language-like robot programming, while maintaining the expressive power and precision of a formal language. Our experiments show that the synthesis method is precise, quick, and asks only a few questions to the users, and we demonstrate in a case study how LtlTalk generalizes from the synthesized tasks to other, yet unseen, tasks.

This work describes the formalization of a word structure grammar that represents the complex morphological and morphosyntactic information embedded within the word forms of an agglutinative language (Basque), giving a comprehensive linguistic description of the main morphological phenomena, such as affixation, derivation, and composition, and also taking into account the modeling of both standard and non-standard words. We have identified the relevant issues to be addressed in the representation of such a grammar.We also present the development of Morfeus+, a tool for the analysis of unrestricted texts, testing its applicability and showing that its coverage is wide and robust, allowing the efficient processing of big volumes of data.This paper describes a mature system that has required several person/years and that tries to integrate a rigorous linguistic specification together with more practical implementation matters, such as the appropriate treatment of unknown words in unrestricted texts.

This study systematically reviews existing approaches to unsupervised grammar induction in terms of their theoretical underpinnings, practical implementations and evaluation. Our motivation is to identify the influence of functional-cognitive schools of grammar on language processing models in computational linguistics. This is an effort to fill any gap between the theoretical school and the computational processing models of grammar induction. Specifically, the review aims to answer the following research questions: Which types of grammar theories have been the subjects of grammar induction? Which methods have been employed to support grammar induction? Which features have been used by these methods for learning? How were these methods evaluated? Finally, in terms of performance, how do these methods compare to one another? Forty-three studies were identified for systematic review out of which 33 described original implementations of grammar induction; three provided surveys and seven focused on theories and experiments related to acquisition and processing of grammar in humans. The data extracted from the 33 implementations were stratified into 7 different aspects of analysis: theory of grammar; output representation; how grammatical productivity is processed; how grammatical productivity is represented; features used for learning; evaluation strategy and implementation methodology. In most of the implementations considered, grammar was treated as a generative-formal system, autonomous and independent of meaning. The parser decoding was done in a non-incremental, head-driven fashion by assuming that all words are available for the parsing model and the output representation of the grammar learnt was hierarchical, typically a dependency or a constituency tree. However, the theoretical and experimental studies considered suggest that a usage-based, incremental, sequential system of grammar is more appropriate than the formal, non-incremental, hierarchical view of grammar. This gap between the theoretical as well as experimental studies on one hand and the computational implementations on the other hand should be addressed to enable further progress in computational grammar induction research.

Machine Translation is a mechanism of transforming text from one language to another with the help of computer technology. Earlier in 2018, a machine translation system had been developed by the authors that translate Sanskrit text to Universal Networking Language expressions and was named as SANSUNL. The work presented in this paper is an extension of SANSUNL system by enhancing POS tagging, Sanskrit language processing and parsing. A Sanskrit stemmer having 23 prefixes and 774 suffixes with grammar rules are used for stemming the Sanskrit sentence in the proposed system. Bidirectional long short-term memory (Bi-LSTM) and stacked LSTM deep neural network models have been used for part of speech tagging of the input Sanskrit text. A tagged dataset of around 400 k entries for Sanskrit have been used for training and testing the neural network models. Proposed Sanskrit context-free grammar has been used with CYK parser to perform the parsing of the input sentence. Size of the Sanskrit-Universal Word dictionary has been increased from 15000 to 25000 entries. Approximately 1500 UNL generation rules have been used to resolve the 46 UNL relations. Four datasets UC-A1, UC-A2, Spanish server gold standard dataset, and 500 Sanskrit sentences taken from the general domain have been used for validating the system. The proposed system is evaluated on BLEU and Fluency score metrics and has reported an efficiency of 95.375%.

This study entitles “A Syntactic Analysis on Noun Phrase Used in English Song by Ed Sheeran Album”. This study analysis of Noun Phrase by using Jumanto’s theory that indicate in his arrangement. This study focused only on the discussion underlined and coding of types of noun phrase. It was found that there are 118 noun phrase im 93 lyrics from the five selected song in the english song lyrics by Ed Sheeran album. There are 92 premodifier or 78%, 4 post modifier or 3,4%, and 23 premodifier and postmodifier at the same time or 18,6%. The most dominant used of noun phrase in the English song was premodifierthat occurred by 92 data (78%). The finding of the analysis shows that the English Song by Ed Sheeran Album has all types of Noun Phrase. The arrangement by Jumanto’s theory it is easy to provide representation of the noun phrase.

The object-relational impedance mismatch (ORIM) problem characterises differences between the object-oriented and relational approaches to data access. Queries generated by object-relational mapping (ORM) frameworks are designed to overcome ORIM difficulties and can cause performance concerns in environments which use object-oriented paradigms. The aim of this paper is twofold, first presenting a survey of database practitioners on the effectiveness of ORM tools followed by an experimental investigation into the extent of operational concerns through the comparison of ORM-generated query performance and SQL query performance with a benchmark data set. The results show there are perceived difficulties in tuning ORM tools and distrust around their effectiveness. Through experimental testing, these views are validated by demonstrating that ORMs exhibit performance issues to the detriment of the query and the overall scalability of the ORM-led approach. Future work on establishing a system to support the query optimiser when parsing and preparing ORM-generated queries is outlined.

Type inference is a feature that is common to a variety of programming languages. While, in the past, it has been prominently present in functional ones (e.g., ML and Haskell), today, many object-oriented/multi-paradigm languages such as C# and C++ offer, to a certain extent, such a feature. Nevertheless, type inference still is an unexplored subject in the realm of C. In particular, it remains open whether it is possible to devise a technique that encompasses the idiosyncrasies of this language. The first difficulty encountered when tackling this problem is that parsing C requires, not only syntactic, but also semantic information. Yet, greater challenges emerge due to C’s intricate type system. In this work, we present a unification-based framework that lets us infer the missing struct, union, enum, and typedef declarations in a program. As an application of our technique, we investigate the reconstruction of partial programs. Incomplete source code naturally appears in software development: during design and while evolving, testing, and analyzing programs; therefore, understanding it is a valuable asset. With a reconstructed well-typed program, one can: (i) enable static analysis tools in scenarios where components are absent; (ii) improve precision of “zero setup” static analysis tools; (iii) apply stub generators, symbolic executors, and testing tools on code snippets; and (iv) provide engineers with an assortment of compilable benchmarks for performance and correctness validation. We evaluate our technique on code from a variety of C libraries, including GNU’s Coreutils and on snippets from popular projects such as CPython, FreeBSD, and Git.

JSON parser, that has been developed, is made of two modules, each dedicated to process structure of either an object or an array. In both modules, building step of document model, that represents its structure, is combined with tokenization step. Design and development of algorithm is aimed at improving speed of parsing of documents.