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In this paper, we compare the performance of several popular pre-trained reference extraction and segmentation toolkits combined in different pipeline configurations on three different datasets. The extraction is end-to-end, i.e. the input is PDF documents, and the output is parsed reference objects. The evaluation is for reference strings and individual fields in the reference objects using alignment by identical fields and close-to-identical values. Our results show that Grobid and AnyStyle perform best of all compared tools, although one may want to use them in combination. Our work is meant to serve as a reference for researchers interested in applying out-of-the-box reference extraction and -parsing tools, for example, as a preprocessing step to a more complex research question. Our detailed results on different datasets with results for individual parsed fields will allow them to focus on aspects that are particularly important to them. [ABSTRACT FROM AUTHOR]

This article evaluates the predictions of an algorithmic‐level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational‐level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational‐level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times. [ABSTRACT FROM AUTHOR]

User interactivity can be enhanced in agricultural measurement and control systems, especially with the continuous advancements in natural language semantic processing. It is necessary to improve user-friendliness in control and query operations within the agricultural measurement and control field, in order to reduce the user operating costs. Firstly, a precise interface of human-computer interaction can be constructed to tailor for the agricultural domain, in order to efficiently translate the user's natural language input into understandable commands for the computer system. The current agricultural field has relied mainly on graphical user interfaces to meet human-computer interaction. But some limitations still remained over time, e.g., the high complexity of human-computer interaction and the low efficiency. Therefore, natural language interface (NLI) has been designed to establish the mapping between natural language from the nature of human-computer interaction. Agricultural measurement and control systems have been considered as the efficient strategy. Among them, the primary task of natural language understanding (NLU) is often used to transform the human language into computer-understandable structured expressions, in order to accurately capture the user's intention and semantics. Deep learning has been utilized to name entity recognition tasks in recent years. Relational components of sentences can be extracted to identify the sentence actions, and then incorporate the annotations of semantic roles, in order to understand the utterances for the computers. Entity recognition has distinctly realized the entity features in the specific domains. Commonly-named entities are usually characterized by fuzzy boundaries in the field of agricultural measurement and control systems. Some challenges remain in the quality of data and the accuracy of annotations, due to the relatively scarce data. It is important to directly apply to the agricultural measurement and control system. In this study, the agricultural measurement and control natural language interface (AMC-NLI) was presented to serve as the natural language interface for the agricultural measurement and control. The users were allowed to operate and control systems using natural language commands. These commands were interpreted using OPERATE, PLACE, and OBJECT attributes within the operate-place-object (OPO) ternary structure, and then transmitted to the gateways, nodes, or devices. Significant semantic information was previously lost using conventional methods when extracting entities from natural language commands, particularly when the commands contained multiple entities of the same type. Additionally, the entity order was confounded on the semantic relationships. A semantic parsing model called BERT-BiLSTM-ATT-CRF-OPO was proposed for the recognition tasks of the named entity in the command parsing of the measurement and control system. BERT pre-trained language models were utilized for the word embedding to enhance contextual understanding. The bidirectional long short-term memory networks (BiLSTM) were employed to capture the semantic features of long sentences and long-distance dependent information. An attention mechanism was incorporated to prioritize the features related to named entities for better local feature extraction. Conditional Random Field (CRF) was utilized to learn the labeling constraints and output globally optimal labeled sequences. The experimental results show that the BERT-BiLSTM-ATT-CRF-OPO model achieved a recognition accuracy of 92.13%, a recall of 93.12%, and an F1 score of 92.76% for the three types of entities. The improved model performed well in the AMC-NLI agricultural measurement and control command interaction, with the accuracy, precision, recall, F-value, and average maximum response time reaching 91.63%, 92.77%, 92.48%, 91.74%, and 2.45s, respectively. The human-computer interaction was enhanced in the agricultural measurement and control system, in order to improve the recognition accuracy of command entity. The finding can offer novel insights into Chinese command parsing, indicating the potential application of natural language processing in agriculture. A more user-friendly and efficient humancomputer interaction was provided for future agricultural measurement and control systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study delves into the syntactic parsing abilities of children and infants exposed to Catalan as their first language. Focusing first on ages 3 to 6, we conducted two sentence-picture matching tasks. In experiment 1, 3 to 4-year-old children failed in identifying singular third-person subjects within null-subject sentences, although they performed above chance in all other scenarios, including plural third-person subjects and sentences with overt full DP subjects. This is reminiscent of the results of Pérez-Leroux for Spanish. In experiment 2, with the same design but involving numeral distractors, children's performance was above chance level across all conditions from age 3 to 4. Then, in experiment 3, we moved to a younger age range with the help of eye-tracking techniques. The findings revealed that infants at 22 months had the ability to parse subject–verb agreement in sentences with third-person null subjects, and at 19 months there was evidence of parsing for third-person plural null subjects. These findings are inconsistent with the perception of children grappling with syntactic agreement computation. We argue that instances of underperformance in subject–verb agreement parsing identified in the literature often stem from task-related and pragmatic issues rather than core syntactic delay. If so, the putative asymmetry between early production of verbal inflection and late comprehension disappears; rather, the results suggest early establishment of matching operations and mastery of language-specific agreement properties before production starts. [ABSTRACT FROM AUTHOR]

In our study, we examine children's and adults' interpretation of argument wh-questions requiring syntactic revision using a questions-after-stories procedure, leading to three major findings. First, children aged 7–8 years, but not 5–6-year-olds, were found to preferably attach the fronted wh-element to the first available gap, rather than the second, indicating an adultlike incremental parsing strategy of active gap filling. Second, older children show a similar rate of revision as adults, higher than younger children, although a substantial rate of misinterpretations remains. Third, we find a significant link between the rate of revision and cognitive control skills measured with an N-back task. Implications for theories of language development and parsing are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Humanities & Educational Sciences Journal is the property of Humanities & Educational Sciences Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sentence processing is a fundamental and critical area of inquiry within the field of language processing, as it plays an essential role in facilitating effective communication. The present study focuses on the immediate processing of sentences in English language communication. The article begins by providing an overview of the basic principles underlying sentence processing. It then delves into the various parsing strategies, including the late closure strategy and the minimal attachment strategy. The modular and interactive models of sentence processing are also presented and discussed. Additionally, the relationship between sentence processing and meaning is explored, with an examination of the role of factors such as presupposition and plausibility in language communication process. Finally, the role of prosody in language processing is expounded upon. The paper draws several conclusions regarding the nature of immediate sentence processing in language communication. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tikrit University for Humanities is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A central topic in psycholinguistics is the study of how and when the parser assigns an antecedent to referentially-dependent elements. One such referentially-dependent element is the null subject of non-finite clauses. The aim of the present study was to examine the role of verb control information in the assignment of an antecedent to such a null subject. The results so far are inconclusive. Some authors argue that verb control information has a late influence, whereas others argue that such verbspecific information has a very rapid influence. We report a self-paced reading study in Spanish in which verb type (subject vs. object control) and grammaticality (grammatical vs. ungrammatical) were manipulated. The grammaticality manipulation was carried out by introducing a person anomaly at the infinitive itself, and not at a later word (e.g., "Te prometí/aconsejé adelgazarme/adelgazarte cinco quilos en un mes." Literal translation, "I to you promised/advised to losemyself/yourself five kilos in a month"). With such a manipulation we can examine whether at the first possible point (i.e., the infinitive) verb control information was used to assign the correct antecedent (i.e., the subject in sentences with a subjectcontrol verb, and the object in sentences with an object-control verb) to PRO. The results showed that at the infinitive there was a main effect of grammaticality, meaning that the correct antecedent has already been assigned to PRO. The present findings are consistent with models that assume that verb-specific information plays an important role in the initial stages of sentence processing. [ABSTRACT FROM AUTHOR]

Copyright of Herald of Journalism / Habaršy Žurnalistika Seriâsy is the property of Al-Farabi Kazakh National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speaker’s intended meaning. This rapid process is subject to multidimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro-/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use of artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain.

A central topic in psycholinguistics is the study of how and when the parser assigns an antecedent to referentially-dependent elements. One such referentially-dependent element is the null subject of non-finite clauses. The aim of the present study was to examine the role of verb control information in the assignment of an antecedent to such a null subject. The results so far are inconclusive. Some authors argue that verb control information has a late influence, whereas others argue that such verb-specific information has a very rapid influence. We report a self-paced reading study in Spanish in which verb type (subject vs. object control) and grammaticality (grammatical vs. ungrammatical) were manipulated. The grammaticality manipulation was carried out by introducing a person anomaly at the infinitive itself, and not at a later word (e.g., “Te prometí/aconsejé adelgazarme/adelgazarte cinco quilos en un mes.” Literal translation, “I to you promised/advised to losemyself/yourself five kilos in a month”). With such a manipulation we can examine whether at the first possible point (i.e., the infinitive) verb control information was used to assign the correct antecedent (i.e., the subject in sentences with a subject-control verb, and the object in sentences with an object-control verb) to PRO. The results showed that at the infinitive there was a main effect of grammaticality, meaning that the correct antecedent has already been assigned to PRO. The present findings are consistent with models that assume that verb-specific information plays an important role in the initial stages of sentence processing.

This research discusses the words used in the first 50 verses of Surah Al-Baqarah. It was noted that there are different grammatical interpretations of the words examined in the sources of parsing (i'rȃb) of the Qur'an during the research. The specific evaluations were discussed in deduction, comparison, and interpretation. In this study, first, the definition of parsing, its importance, the relationship between parsing is expressed and then other issues concerning its importance, the relationship between parsing in the Qur'an, negative parsing, and parsing, and the study of the concept of parsing and attention are revealed. The historical process of parsing is also examined. Then, the Arabic text and meaning of the verses are revealed in their entirety, and the various grammatical interpretations of the relevant words are mentioned. As a result of the research, the role of i'rȃb knowledge in determining the correct meaning was pointed out by drawing attention to the fact that it is possible to analyze a certain element in different ways in terms of grammar and that this is an important method in reaching different meanings. The role of knowledge of parsing in determining the correct meaning is also emphasized. Giving that the primary purpose of Islamic sciences is to understand the message of the Qur'an in the most accurate way. The presence of different forms of parsing in the verses affects the meaning. For this reason, a good knowledge of parsing is needed to understand the Qur'an correctly. [ABSTRACT FROM AUTHOR]

Automated log parsing is essential for many log-mining applications, as logs provide a vast range of information on events and variations within an operating system or software at runtime. Over the years, various methods have been proposed for log parsing. With improved log-parsing methods, log-mining applications can gain deeper insights into system behaviors and identify anomalies or failures promptly. However, current log parsers still face limitations, such as insufficient parsing of log templates and a lack of parallelism, as well as inaccurate log template parsing. To overcome these limitations, we have designed Polo, a parser that leverages a prefix forest composed of ternary search trees to mine templates from logs. We then conducted extensive experiments to evaluate the accuracy of Polo on nine representative system logs, achieving an average accuracy of 0.987. It is 9.93% to 40.95% faster than the state-of-the-art parsing methods. Furthermore, we evaluated our approach on a downstream log analysis task, specifically anomaly detection. The experimental results demonstrated that, in terms of F1-score, our parser outperformed Deeplog, LogAnomaly, CNN, and LogRobust by 11.5%, 4%, 1%, and 19.1%, respectively, exhibiting a promising recall score of 0.971. These results indicate the effectiveness of Polo for anomaly detection. [ABSTRACT FROM AUTHOR]

Music can be interpreted by attributing syntactic relationships to sequential musical events, and, computationally, such musical interpretation represents an analogous combinatorial task to syntactic processing in language. While this perspective has been primarily addressed in the domain of harmony, we focus here on rhythm in the Western tonal idiom, and we propose for the first time a framework for modeling the moment‐by‐moment execution of processing operations involved in the interpretation of music. Our approach is based on (1) a music‐theoretically motivated grammar formalizing the competence of rhythmic interpretation in terms of three basic types of dependency (preparation, syncopation, and split; Rohrmeier, 2020), and (2) psychologically plausible predictions about the complexity of structural integration and memory storage operations, necessary for parsing hierarchical dependencies, derived from the dependency locality theory (Gibson, 2000). With a behavioral experiment, we exemplify an empirical implementation of the proposed theoretical framework. One hundred listeners were asked to reproduce the location of a visual flash presented while listening to three rhythmic excerpts, each exemplifying a different interpretation under the formal grammar. The hypothesized execution of syntactic‐processing operations was found to be a significant predictor of the observed displacement between the reported and the objective location of the flashes. Overall, this study presents a theoretical approach and a first empirical proof‐of‐concept for modeling the cognitive process resulting in such interpretation as a form of syntactic parsing with algorithmic similarities to its linguistic counterpart. Results from the present small‐scale experiment should not be read as a final test of the theory, but they are consistent with the theoretical predictions after controlling for several possible confounding factors and may form the basis for further large‐scale and ecological testing. [ABSTRACT FROM AUTHOR]

In the Fourth Industrial Revolution, crime is hardly reported to the Police, or other law enforcement agencies. Most victims prefer to go to Social Media and vent, as this medium is easier for them to access and requires no paperwork or interrogations. This trend leaves policy makers and the law enforcers with skewed dataset, due to unreported crimes. Hence, it is paramount that one finds a way to “mine” the crime data reported on social media. In this paper, we have attempted to estimate crime rates, using Twitter as a data source. To do this, we have used a formal technique — Jumping Finite Automata (JFA), for the abstraction of a corpus of crimerelated words and used shuffle algorithms to establish semantic relationships between these words. The JFA was implemented in a tool called “Crime-Ripper”. Crime-Ripper uses tweets retrieved from crime hashtags on Twitter to estimate crime rates and produce reports that are map annotations, showing areas of a city and their respective estimated crime-rates. CrimeRipper is expected to find applications in law enforcement, policy making and public safety sensitization. [ABSTRACT FROM AUTHOR]

Copyright of Umm Al-Qura University Journal for Languages & Literature is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We describe in this paper the adopted approach combining a phrase structure grammar and dependency rules to develop AlkhalilPArser. The general architecture of this parser is composed of three main levels. The first level includes basic tasks such as tokenization, part-of-speech tagging, and chunking. The next level deals with analysis tasks such as managing common, internal and external dependencies according to the nature of the proposal. The last level detects and corrects anomalies. This parser is an extension to verbal sentences of a system previously designed for parsing the nominal sentences. Several modifications have been made to adapt this system to all types of sentences and improve its accuracy. The tests carried out on a representative corpus and the comparisons with other parsers testify to the robustness of our system. [ABSTRACT FROM AUTHOR]

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence‐initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent‐initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400‐like negativity when the disambiguation was toward patient‐initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents. [ABSTRACT FROM AUTHOR]

Copyright of Djoussour El-maarefa is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad‐coverage tools from natural‐language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context‐free grammars (CFGs), yet such formalisms are not sufficiently expressive for human languages. Combinatory categorial grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work, we evaluate whether a more expressive CCG provides a better model than a CFG for human neural signals collected with functional magnetic resonance imaging (fMRI) while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next‐word predictability from a transformer neural network language model. Such a comparison reveals unique contributions of CCG structure‐building predominantly in the left posterior temporal lobe: CCG‐derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure‐building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds. [ABSTRACT FROM AUTHOR]

Summary: In recent work, Lemire (2021) presented a fast algorithm to convert number strings into binary floating‐point numbers. The algorithm has been adopted by several important systems: for example, it is part of the runtime libraries of GCC 12, Rust 1.55, and Go 1.16. The algorithm parses any number string with a significand containing no more than 19 digits into an IEEE floating‐point number. However, there is a check leading to a fallback function to ensure correctness. This fallback function is never called in practice. We prove that the fallback is unnecessary. Thus we can slightly simplify the algorithm and its implementation. [ABSTRACT FROM AUTHOR]