Syntactic Ambiguity Research Articles

Learning Stress with Feet and Grids

This paper investigates quantity-insensitive stress learning using the MaxEnt learner of Pater and Prickett (2022) and compares the performance of the learner equipped with three different constraint sets: a foot-based constraint set and two grid-based constraint sets, one drawn directly from Gordon (2002), and one that changes the formulation of the main stress constraint to match the foot-based learner. The learner equipped with the foot-based constraint set succeeds at learning all the languages from the Gordon (2002) typology that it can represent; the structural ambiguity of the foot-based representations is not a problem in this regard. The foot-based learner also learns the languages as quickly in terms of number of epochs as the faster of the grid-based learners, which is the one with the revised main stress constraint. We conclude that the foot-based learner and the grid-based learner fare similarly well in this initial comparison on a typologically grounded set of learning problems.

Púnandtio̍hin Penang Hokkien

AbstractPenang Hokkien was spoken by the early Hokkien settlers as a heritage language in a Malay dominant environment before it became a lingua franca of the Chinese communities in present-day Penang and its neighboring states in Northern Malaysia. Over two centuries, Penang Hokkien has assimilated Malay words and structures. This article discusses the forms and functions ofpúnandtio̍h, which have assimilated Malay elements as response strategies to two key triggers for deviation from the baseline Southern Min dialect: ambiguity and complex structure. Through a detailed study ofpúnandtio̍h, this article contributes to a better understanding of the development of a Malay-dominant heritage language that is based on a Chinese dialect spoken in the Malay peninsula.

Polar Nitride Perovskite LaWN3-δ with Orthorhombic Structure.

Nitride perovskite LaWN3 has been predicted to be a promising ferroelectric material with unique properties for diverse applications. However, due to the challenging sample preparation at ambient pressure, the crystal structure of this nitride remains unsolved, which results in many ambiguities in its properties. Here, the authors report a comprehensive study of LaWN3 based on high-quality samples synthesized by a high-pressure method, leading to a definitive resolution of its crystal structure involving nitrogen deficiency. Combined with theoretical calculations, these results show that LaWN3 adopts an orthorhombic Pna21 structure with a polar symmetry, possessing a unique atomic polarization along the c-axis. The associated atomic polar distortions in LaWN3 are driven by covalent hybridization of W: 5d and N: 2p orbitals, opening a direct bandgap that explains its semiconducting behaviors. The structural stability and electronic properties of this nitride are also revealed to be closely associated with its nitrogen deficiency. The success in unraveling the structural and electronic ambiguities of LaWN3 would provide important insights into the structures and properties of the family of nitride perovskites.

A framework for doctoral education in developing students’ mental well-being by integrating the demand and resources of the program: An integrative review

Background: Research on doctoral students’ mental well-being has gained significant importance in recent years. The findings of such studies were uncertain about the critical demands and resources of a doctoral program that substantially influence the students’ mental health. This review aims to integrate the current evidence in bringing out the nature and significance of differentiated demands, contextual and personal resources, and their influence on the well-being of the students. Methods: An integrative literature review was conducted based on the five-stage framework of Whittemore and Knafl. The study identified 45 articles published from 2000 onwards following the Joanna Briggs Institute quality evaluation criteria and PRISMA reporting guidelines for selecting eligible articles. Results: The integrative review findings divulge that differentiated demands of doctoral programs were categorized into challenge-hindrance demands. The differentiated demands experienced by doctoral students were grouped as ambiguity in doctoral program structure, resource inadequacy, workload, complexity, and responsibility. Additionally, institutional support, research supervisory support, and intrinsic motivation were treated as essential resource in mitigating the effects of the differentiated demands of the doctoral program. Conclusions: An integrated conceptual model was built exclusively for doctoral programs and suggests that the universities and supervisors design and structure healthy, constructive doctoral programs. As an outcome of the review, theoretical underpinnings of demands-resources and mental well-being are reported. The current review is an initial attempt to synthesize challenge-hindrance demands and contextual-personal resources in determining the mental well-being of doctoral students.

Analysis of the First Ion Coordination Sphere: A Toolkit to Analyze the Coordination Sphere of Ions.

Rapid and accurate approaches to characterizing the coordination structure of an ion are important for designing ligands and quantifying structure-property trends. Here, we introduce AFICS (Analysis of the First Ion Coordination Sphere), a tool written in Python 3 for analyzing the structural and geometric features of the first coordination sphere of an ion over the course of molecular dynamics simulations. The principal feature of AFICS is its ability to quantify the distortion a coordination geometry undergoes compared to uniform polyhedra. This work applies the toolkit to analyze molecular dynamics simulations of the well-defined coordination structure of aqueous Cr3+ along with the more ambiguous structure of aqueous Eu3+ chelated to ethylenediaminetetraacetic acid. The tool is targeted for analyzing ions with fluxional or irregular coordination structures (e.g., solution structures of f-block elements) but is generalized such that it may be applied to other systems.

Synthesis, Assembly, and Applications of Magic-Sized Semiconductor (CdSe)13 Cluster.

ConspectusAtomically precise metal chalcogenide clusters (MCCs) are model molecular compounds of scientifically and technologically important semiconductor nanocrystals, which are known as quantum dots (QDs). The significantly high ambient stability of MCCs of particular sizes, as compared to that of slightly smaller or larger sizes, made them be termed "magic-sized clusters" (MSCs). In other words, MSCs with specific sizes between sizes of precursors (typically, metal-ligand complexes) and nanocrystals (typically, QDs) appear sequentially during the colloidal synthesis of nanocrystals, while the other cluster species decompose to precursor monomers or are consumed during the growth of the nanocrystals. Unlike nanocrystals with an ambiguous atomic-level structure and a substantial size distribution, MSCs possess atomically monodisperse size, composition, and distinct atomic arrangement. Chemical synthesis and exploration of properties of MSCs are of great significance since they help systematically understand the evolution of fundamental properties as well as build structure-activity relationships at distinct molecular levels. Furthermore, MSCs are anticipated to offer atomic-level insights into the growth mechanism of the semiconductor nanocrystals, which is highly desirable in the design of advanced materials with new functions. In this Account, we cover our recent efforts in the advancement of one of the most important stoichiometric CdSe MSCs, (CdSe)13. In particular, we present its molecular structure derived from a single crystal X-ray crystallographic study of the closest MSC, Cd14Se13. The crystal structure of MSC enables not only the understanding of the electronic structure and prediction of the potential sites for heteroatom dopants (e.g., Mn2+ and Co2+) but also the identification of favorable synthetic conditions for the selective synthesis of desired MSCs. Next, we focus on enhancing the photoluminescence quantum yield and stability of Mn2+ doped (CdSe)13 MSCs through their self-assembly, which is facilitated by the rigid diamines. In addition, we show how atomic-level synergistic effects and functional groups of the assemblies of alloy MSCs can be utilized for a highly enhanced catalytic CO2 fixation with epoxides. Benefiting from the intermediate stability, the MSCs are explored as single-source precursors to low-dimensional nanostructures, such as nanoribbons and nanoplatelets, through the controlled transformation. Distinct differences in the outcome of the solid-state and colloidal-state conversion of MSCs suggest the need for careful consideration of the phase and reactivity of MSCs as well as the type of dopant to achieve novel structured multicomponent semiconductors. Finally, we summarize the Account and provide future perspectives on the fundamental and applied scientific research of MSCs.

SSEMGAT: Syntactic and Semantic Enhanced Multi-Layer Graph Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis aims to identify the sentiment polarity of specific aspects appearing in a given sentence or review. The model based on graph structure uses a dependency tree to link the aspect word with its corresponding opinion word and achieves significant results. However, for some sentences with ambiguous syntactic structure, it is difficult for the dependency tree to accurately parse the dependencies, which introduces noise and degrades the performance of the model. Based on this, we propose a syntactic and semantic enhanced multi-layer graph attention network (SSEMGAT), which introduces constituent trees in syntactic features to compensate for dependent trees at the clause level, exploiting aspect-aware attention in semantic features to assign the attention weight of specific aspects between contexts. The enhanced syntactic and semantic features are then used to classify specific aspects of sentiment through a multi-layer graph attention network. Accuracy and Macro-F1 are used as evaluation indexes in the SemEval-2014 Task 4 Restaurant and Laptop dataset and the Twitter dataset to compare the proposed model with the baseline model and the latest model, achieving competitive results.

John Walton’s Invention of the Iambic Pentameter

ABSTRACT When Geoffrey Chaucer died in 1400, the decasyllable he invented in the late 1370s and raised to prominence in the 1380s and 1390s lay precariously in the hands of scribes and rivals. Of the poets to reform Chaucer’s meter in the first decade of the fifteenth century, John Walton devised the subtlest and most successful alternative to the inherited decasyllable, capitalizing on the full range of formal and grammatical ambiguities in Chaucer’s line and turning them from bugs into features, in an expanding program of new candidate meters. His revision of the decasyllable in De Consolatione Philosophiae (ca. 1410), a verse translation of Boethius and veiled satire on Chaucer’s Boece, uses the decasyllabic resource to fill several vacancies in the scramble for authority following Chaucer’s death.

Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

Nanoscale chirality is an actively growing research field spurred by the giant chiroptical activity, enantioselective biological activity, and asymmetric catalytic activity of chiral nanostructures. Compared to chiral molecules, the handedness of chiral nano- and microstructures can be directly established via electron microscopy, which can be utilized for the automatic analysis of chiral nanostructures and prediction of their properties. However, chirality in complex materials may have multiple geometric forms and scales. Computational identification of chirality from electron microscopy images rather than optical measurements is convenient but is fundamentally challenging, too, because (1) image features differentiating left- and right-handed particles can be ambiguous and (2) three-dimensional structure essential for chirality is 'flattened' into two-dimensional projections. Here, we show that deep learning algorithms can identify twisted bowtie-shaped microparticles with nearly 100% accuracy and classify them as left- and right-handed with as high as 99% accuracy. Importantly, such accuracy was achieved with as few as 30 original electron microscopy images of bowties. Furthermore, after training on bowtie particles with complex nanostructured features, the model can recognize other chiral shapes with different geometries without retraining for their specific chiral geometry with 93% accuracy, indicating the true learning abilities of the employed neural networks. These findings indicate that our algorithm trained on a practically feasible set of experimental data enables automated analysis of microscopy data for the accelerated discovery of chiral particles and their complex systems for multiple applications.

Rendering English Structural Ambiguity into Arabic and the Role of Transformational Syntax along with Arabic Morphology

Rendering English Structural Ambiguity into Arabic and the Role of Transformational Syntax along with Arabic Morphology

A-maze of Natural Stories: Comprehension and surprisal in the Maze task

Behavioral measures of word-by-word reading time provide experimental evidence to test theories of language processing. A-maze is a recent method for measuring incremental sentence processing that can localize slowdowns related to syntactic ambiguities in individual sentences. We adapted A-maze for use on longer passages and tested it on the Natural Stories corpus. Participants were able to comprehend these longer text passages that they read via the Maze task. Moreover, the Maze task yielded useable reaction time data with word predictability effects that were linearly related to surprisal, the same pattern found with other incremental methods. Crucially, Maze reaction times show a tight relationship with properties of the current word, with little spillover of effects from previous words. This superior localization is an advantage of Maze compared with other methods. Overall, we expanded the scope of experimental materials, and thus theoretical questions, that can be studied with the Maze task.

An Overview of Context Capturing Techniques in NLP

In the NLP context identification has become a prominent way to overcome syntactic and semantic ambiguities. Ambiguities are unsolved problems but can be reduced to a certain level. This ambiguity reduction helps to improve the quality of several NLP processes, such as text translation, text simplification, text retrieval, word sense disambiguation, etc. Context identification, also known as contextualization, takes place in the preprocessing phase of NLP processes. The essence of this identification is to uniquely represent a word or a phrase to improve the decision-making during the transfer phase of the NLP processes. The improved decision-making helps to improve the quality of the output. This paper tries to provide an overview of different context-capturing mechanisms used in NLP.

The arch. Crisis and its form

The arch, a favourite form of world architecture, is analysed in terms of its phenomenology, genesis and meanings. There is a thesis about the ambiguity of the arched structure, its initial criticality, which allows us to consider the arch as an architectural symbol of crisis. The popularity of the arch implies that architecture tends to signify states of crisis, to “play” with them, to give any crisis. . . an expressive form.

GSAL: Geometric structure adversarial learning for robust medical image segmentation

Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics (e.g., inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning ”focus, path, and difficulty.” The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators, which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks, including polyp segmentation, COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics. The code is available at: https://github.com/DLWK/GSAL.

I saw a movie with Brad Pitt – Using pencils to learn syntax

This paper presents a research project conducted on four second-year English classes (approximately 110 students) at Sorbonne Nouvelle University (Paris, France) in 2021/2022. This project was motivated by the realization that students were struggling to understand the opposition between linearity and syntax and to grasp the basic syntax of noun phrases in English. This difficulty can be explained by two reasons: the students were not engaged in the learning process because they did not clearly understand what they had to learn about syntax; and what was expected of them – demonstrating their understanding of noun phrase structure by adding brackets – was beyond their reach.The authors assumed that a non-verbal modality such as drawing could serve as a first step in understanding the opposition between syntax and linearity. They hypothesized that offering more than one modality to demonstrate one’s understanding of noun phrase structure would address both difficulties: engaging the students and helping them achieve the learning outcomes. To that effect, they designed an activity that introduced the concept of syntactic ambiguity and that was both engaging and multimodal. Students were presented with syntactically ambiguous sentences such as "I saw a movie with Brad Pitt" and were asked to:• discuss the two possible interpretations in their own words, i.e., produce a gloss;• draw the two possible interpretations of the sentence; • add a follow-up sentence to clarify the context;• bracket the noun phrases to make the syntactic pattern visible.A satisfaction questionnaire was completed by 93 students at the end of the session and 111 midterm exam papers were collected for analysis.The results tend to prove that the original hypothesis was valid. Offering an array of mediums, including drawing, to describe the syntax of noun phrases made students feel confident and engaged. However, the lesson plan should also include activities that help students transition towards verbal expression as university students are expected to exhibit understanding in words.

Sacred, Communal or Private “Oikoi”? Ambivalent edifices of the Archaic period on the island of Despotiko in the Cyclades

In the frame of the Research Project Sacred, Public or Private Buildings? Ambiguous sites and structures in the Early Cyclades, a strict methodology has been adopted, aiming at deciphering the “character” of a number of buildings. The adopted methodology has been based on the detailed documentation of the architectural remains and the related finds. In the former case, the photogrammetry of the buildings, including ground plans and wall sections, has been combined with a structural analysis mainly involving the descriptive representation of the architectural remains and the examination of the techniques and materials used during the construction process. The various types of finds from each building have not been only classified according to their types, but an emphasis has been placed on their spatial distribution revealed through the use of the Geographic Information System (GIS). On this basis, a synthetic study both of the architectural remains and of the artefacts in their original context was possible, elucidating the function of the selected buildings and their components. At the same time, a list of material correlates was created to provide a basis as secure as possible for designating each building’s “character”.

Multifaceted Dimensions of Ambiguity in English

Language is an effective means of communication. Words, phrases, and sentences are the standard units of a language. Every word is associated with a particular meaning/sense. Sometimes the meaning of a word undergoes a change, depending on the context or the structure. This triggers ambiguity. When a word, a phrase or a sentence is said to have more than one meaning or multiple meaning, they are said to be ambiguous. Homophonous or polysemous words can give rise to ambiguity depending on the context. There are various types of ambiguity in English. They include lexical ambiguity, syntactic/structural ambiguity, pragmatic ambiguity, phonological ambiguity, scope ambiguity, anaphoric/ referential ambiguity, semantic ambiguity, etc. Lexical ambiguity arises due to polysemous words. Structural ambiguity occurs when a phrase, clause or a sentence possesses more than one meaning. Phonological ambiguity arises when homophonous words—words having identical sounds –are used. Anaphoric ambiguity is perceived when there is more than one potential antecedent in the text. Scope ambiguity is found when a structure has two or more than two quantifiers or floating quantifiers and conveys more than one meaning. When the functional value of a phrase or a sentence changes, we find pragmatic ambiguity. When a sentence or a structure has more than two meanings, it contains multiple ambiguity. Ambiguous words lend a spicy dimension to the use and application of the language. Ambiguous words or phrases or sentences present in a text enhance the value of the text. Ambiguous words, phrases, and sentences are interesting and entertaining. Ambiguity tests our sophisticated cognitive skills for disambiguating. This disambiguation imparts pleasure to us. Ambiguity can assess our competence in the language. It tests the skill of comprehension. We enjoy disambiguating words, phrases and sentences in English from the core of our heart, and our disambiguating these whets our competence in the language.

Whence the Womb?

Abstract In John 7:38, Jesus makes the proclamation ποταµοὶ ἐκ τῆς κοιλίας αὐτοῦ ῥεύσουσιν ὕδατος ζῶντος. Among a variety of other issues—textual problems, ambiguous syntax, and an unclear scriptural referent—an enduring problem for translators has been the word κοιλία. Here, the author argues that the most natural meaning for κοιλία in this context is “womb.” Although governed by the masculine possessive pronoun αὐτοῦ, κοιλία’s most common NT meaning is “womb,” and a survey of Greek literature corroborates “womb” as a viable translation, and preferable to the alternatives in standard Bible translations (such as “heart,” “belly,” or “side”). Reading John 7:38’s κοιλία as “womb” is supported by the immediate context and literary background and sheds further light on the Gospel’s themes of rebirth.

Rational design of heterogenized molecular phthalocyanine hybrid single-atom electrocatalyst towards two-electron oxygen reduction

Single-atom catalysts supported on solid substrates have inspired extensive interest, but the rational design of high-efficiency single-atom catalysts is still plagued by ambiguous structure determination of active sites and its local support effect. Here, we report hybrid single-atom catalysts by an axial coordination linkage of molecular cobalt phthalocyanine with carbon nanotubes for selective oxygen reduction reaction by screening from a series of metal phthalocyanines via preferential density-functional theory calculations. Different from conventional heterogeneous single-atom catalysts, the hybrid single-atom catalysts are proven to facilitate rational screening of target catalysts as well as understanding of its underlying oxygen reduction reaction mechanism due to its well-defined active site structure and clear coordination linkage in the hybrid single-atom catalysts. Consequently, the optimized Co hybrid single-atom catalysts exhibit improved 2e− oxygen reduction reaction performance compared to the corresponding homogeneous molecular catalyst in terms of activity and selectivity. When prepared as an air cathode in an air-breathing flow cell device, the optimized hybrid catalysts enable the oxygen reduction reaction at 300 mA cm−2 exhibiting a stable Faradaic efficiency exceeding 90% for 25 h.

ParsingPhrase: Parsing-based automated quality phrase mining

Phrases represent independent semantics in natural language but usually have indeterminate lengths and different combinations. So, extracting meaningful phrases from unstructured texts will substantially reduce semantic ambiguity and lay the foundation for downstream natural language tasks. Most existing research obtains candidate phrases by N-grams, which includes meaningless word sequences and degrades algorithm performance. In this paper, we propose a novel phrase-mining algorithm, called ParsingPhrase, which effectively extracts combination phrases from text and improves phrase quality by syntactic features. It consists of three stages. Firstly, all sentences in texts are represented as parsing trees by PCFG (Probabilistic Context-Free Grammar). We propose PBMP (Parsing-Based Phrase mining) to obtain candidate phrases from those parsing trees. Then, we introduce a new phrase evaluation indicator, called Significance, that relies on the role of phrases to measure their importance. We integrate the Significance with conventional evaluation indexes for a more reasonable phrase evaluation. Finally, we optimize the phrase quality again by exploiting the optimal phrase composition features of sentences. To the best of our knowledge, it is the first work to employ parsing for combination phrase mining and evaluation, meanwhile offering a solution for syntactic disambiguation. Experiments on three real corpora demonstrate that the ParsingPhrase exceeds state-of-the-art baselines, is 7% higher in the candidate phrase conversion rate, and is 6% better in terms of Precision.