Language Processing Principles Research Articles

MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal-Organic Frameworks.

The remarkable versatility of metal-organic frameworks (MOFs) stems from their rich chemical information, leading to numerous successful applications. However, identifying optimal MOFs for specific tasks necessitates a thorough assessment of their chemical attributes. Conventional machine learning approaches for MOF prediction have relied on intricate chemical and structural details, hampering rapid evaluations. Drawing inspiration from recent advancements exemplified by Snurr et al., wherein a text string was used to represent a MOF (MOFid), we introduce a MOFid-aided deep learning model, named the MOF-GRU model. This model, founded on natural language processing principles and utilizing the gated recurrent unit architecture, leverages the serialized text string representation of metal-organic frameworks (MOFs) to forecast gas separation performance. Through a focused study on CH4/N2 separation, we substantiate the efficacy of this approach. Comparative assessments against traditional machine learning techniques underscore our model's superior predictive accuracy and its capacity to handle extensive data sets adeptly. The MOF-GRU model remarkably uncovers latent structure-performance relationships with only MOF sequences, obviating the necessity for intricate three-dimensional (3D) structural information. Overall, this model's judicious design empowers efficient data utilization, thereby hastening the discovery of high-performance materials tailored for gas separation applications.

Ntelligent Estimation of Social Media Sentimental Features using Deep Learning with Natural Language Processing Strategies

In this digital world, experience sharing, knowledge exploration, taught posting and other related social exploitations are common to every individual as well as social media/network such as Face Book, Twitter, etc plays a vital role in such kinds of activities. In general, many social network based sentimental feature extraction details and logics are available as well as many researchers work on that domain for last few years. But all those research specification are narrowed in the sense of building a way for estimating the opinions and sentiments with respect to the tweets and posts the user raised on the social network or any other related web interfacing medium. Many social network schemes provides an ability to the users to push the voice tweets and voice messages, so that the voice messages may contain some harmful as well as normal and important contents. In this paper, a new methodology is designed called Intensive Deep Learning based Voice Estimation Principle (IDLVEP), in which it is used to identify the voice message content and extract the features based on the Natural Language Processing (NLP) logic. The association of such Deep Learning and Natural Language Processing provides an efficient approach to build the powerful data processing model to identify the sentimental features from the social networking medium. This hybrid logic provides support for both text based and voice based tweet sentimental feature estimations. The Natural Language Processing principles assists the proposed approach of IDLVEP to extracts the voice content from the input message and provides a raw text content, based on that the deep learning principles classify the messages with respect to the estimation of harmful or normal tweets. The tweets raised by the user are initially sub-divided into two categories such as voice tweets and text tweets. The voice tweets will be taken care by the NLP principles and the text enabled tweets will be handled by means of deep learning principles, in which the voice tweets are also extracted and taken care by the deep learning principle only. The social network has two different faces such as provides support to developments as well as the same it provides a way to access that for harmful things. So, that this approach of IDLVEP identifies the harmful contents from the user tweets and remove that in an intelligent manner by using the proposed approach classification strategies. This paper concentrates on identifying the sentimental features from the user tweets and provides the harm free social network environment to the society.

English fragments, Minimize Domains, and Minimize Forms

ABSTRACTWe offer an account of preposition drop under clausal ellipsis in terms of two language processing principles: Minimize Domains and Minimize Forms. We argue that when Minimize Domains operates within the PP domain, it disfavors preposition drop due to the preferred independent processability of the PP fragment. When it operates within the VP domain it favors preposition drop in proportion to the number and strength of semantic dependencies between V and P in a given language: the more dependencies there are, and the stronger they are, the stronger the preference for preposition drop. In this way fragments are avoided with long dependencies between P and a distant V. We demonstrate this pattern in English corpora and propose it as an explanation for the typologically unusual preference that English shows for NP fragments. Minimize Forms supports preposition drop in easy-to-process environments cross-linguistically and in English when the more minimal fragment (NP) can be easily linked to its correlate in the antecedent, disfavoring preposition drop elsewhere. The predictions of Minimize Domains and Minimize Forms receive support from a mixed-effects regression model fitted to data from spoken US English, and can be understood as motivations for construction-specific constraints and preferences in clausal ellipsis.

A Self-Organizing Approach to Subject-Verb Number Agreement.

We present a self-organizing approach to sentence processing that sheds new light on notional plurality effects in agreement attraction, using pseudopartitive subject noun phrases (e.g., a bottle of pills). We first show that notional plurality ratings (numerosity judgments for subject noun phrases) predict verb agreement choices in pseudopartitives, in line with the "Marking" component of the Marking and Morphing theory of agreement processing. However, no account to date has derived notional plurality values from independently needed principles of language processing. We argue on the basis of new experimental evidence and a dynamical systems model that the theoretical black box of notional plurality can be unpacked into objectively measurable semantic features. With these semantic features driving structure formation (and hence agreement choice), our model reproduces the human verb production patterns as a byproduct of normal processing. Finally, we discuss how the self-organizing approach might be extended to other agreement attraction phenomena.

The neurophysiology of language processing shapes the evolution of grammar: evidence from case marking.

Do principles of language processing in the brain affect the way grammar evolves over time or is language change just a matter of socio-historical contingency? While the balance of evidence has been ambiguous and controversial, we identify here a neurophysiological constraint on the processing of language that has a systematic effect on the evolution of how noun phrases are marked by case (i.e. by such contrasts as between the English base form she and the object form her). In neurophysiological experiments across diverse languages we found that during processing, participants initially interpret the first base-form noun phrase they hear (e.g. she…) as an agent (which would fit a continuation like … greeted him), even when the sentence later requires the interpretation of a patient role (as in … was greeted). We show that this processing principle is also operative in Hindi, a language where initial base-form noun phrases most commonly denote patients because many agents receive a special case marker ("ergative") and are often left out in discourse. This finding suggests that the principle is species-wide and independent of the structural affordances of specific languages. As such, the principle favors the development and maintenance of case-marking systems that equate base-form cases with agents rather than with patients. We confirm this evolutionary bias by statistical analyses of phylogenetic signals in over 600 languages worldwide, controlling for confounding effects from language contact. Our findings suggest that at least one core property of grammar systematically adapts in its evolution to the neurophysiological conditions of the brain, independently of socio-historical factors. This opens up new avenues for understanding how specific properties of grammar have developed in tight interaction with the biological evolution of our species.

Constraints on ellipsis alternation: A view from the history of English

AbstractI offer a diachronic perspective on English ellipsis alternation, or the alternation between inclusion and omission of prepositions from remnants under sluicing and bare argument ellipsis. The relative freedom to omit prepositions from remnants has not been stable in English; this freedom is connected to the strength of semantic dependencies between prepositions and verbs. Remnants without prepositions are first attested, but remain less frequent than remnants with prepositions, as late as Early Modern English and gain in frequency following this period. I demonstrate that three constraints—correlate informativity, structural persistence, and construction type—predict ellipsis alternation in Early and Late Modern English. However, predicting ellipsis alternation in present-day English requires semantic dependencies in addition to the three constraints. The constraints can be subsumed under principles of language processing and production (considerations of accessibility, a tendency to reuse structure, and a conventionalized performance preference for efficiently accessing constituents that form processing domains), permitting a unified processing account of ellipsis alternation with cross-linguistic coverage.

Language contact outcomes as the result of bilingual optimization strategies

This paper sketches a comprehensive framework for modeling and interpreting language contact phenomena, with speakers’ bilingual strategies in specific scenarios of language contact as its point of departure. Bilingual strategies are conditioned by social factors, processing constraints of speakers’ bilingual competence, and perceived language distance. In a number of domains of language contact studies important progress has been made, including Creole studies, code-switching, language development, linguistic borrowing, and areal convergence. Less attention has been paid to the links between these fields, so that results in one domain can be compared with those in another. These links are approached here from the perspective of speaker optimization strategies. Four strategies are proposed: maximize structural coherence of the first language (L1); maximize structural coherence of the second language (L2); match between L1 and L2 patterns where possible; and rely on universal principles of language processing. These strategies can be invoked to explain outcomes of language contact. Different outcomes correspond to different interactions of these strategies in bilingual speakers and their communities.

Bilingual strategies from the perspective of a processing model

Muysken argues for four general “strategies” that characterize language contact phenomena across several levels of description. These strategies are (A) maximize structural coherence of the first language (L1); (B) maximize structural coherence of the second language (L2); (C) match between L1 and L2 patterns where possible; and (D) use universal language processing principles. These strategies are seen as choices that bilingual speakers make, individually and collectively, and that are influenced by multiple social, individual, and linguistic factors. This account has the clear advantage of unifying a seemingly very diverse set of language contact phenomena using a limited set of principles. One such phenomenon is cross-linguistic structural priming, the tendency of bilingual speakers to copy grammatical structures from a language recently used to another language (e.g., Hartsuiker, Pickering & Veltkamp, 2004), which Muysken considers an example of “bilingual interference”. In this domain, I will explore how these strategies can be realized in terms of a psycholinguistic processing model, and whether these strategies can be reduced to even more basic principles.

Early language intervention for children with intellectual disabilities: A neurocognitive perspective

For children with intellectual disabilities (ID), stimulation of their language and communication is often not a priority. Advancements in brain research provide guidelines for early interventions aimed at the stimulation of language and communication skills. In the present study, the effectiveness of an early language intervention which draws upon neurocognitive principles of language processing and language learning was assessed. Ten children participated in the intervention and 18 were followed for control purposes. The intervention group showed greater progress than the control group. The higher learning gains for the intervention group were mostly driven by the non-speaking children. However, the progress of the intervention children slowed down significantly following intervention.An early language intervention such as that studied here can accelerate the language development of children with ID. To maintain the effects, however, the intervention should be prolonged in several settings that focus on consecutive learning (e.g., day-care centres and schools).

Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97–1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82–0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.

Een Model Voor Taalcontact

In a number of domains of language contact studies important progress has been made, including Creole studies, code switching and code mixing, second language acquisition, linguistic borrowing, and language areas. Less attention has been paid to the conceptual links between these fields. These links will be the focus of the present paper, which approaches this issue from the perspective of speaker optimization strategies. Four alternative strategies are proposed: optimize the structural coherence of the LI, optimize possible matching between LI and L2 patterns, optimize universal principles of language processing, optimize the structural coherence of the L2. It will be argued that these strategies can be invoked to explain outcomes of language contact, and that different outcomes correspond to different rankings of these strategies by bilingual speakers and the community they belong to.

Text data as evidence for language processing principles: The grammar of ordered events

While most research dealing with language comprehension and production exploits experimental data, the present paper argues that a judicious use of text data can also provide evidence relevant to the operation of various language processing principles. In particular, an investigation is made of those English complex sentences used to represent two ordered events and containing before or after subordinate clauses. Following an analysis of the frequency, position and information distribution of each such structure from some 1331 pages of running text from three different sources, it is argued that the selection of the forms and their distributions in the texts results from the employment of certain syntactic and discourse constraints. It is shown that the GIVEN-NEW constraint is satisfied across all the texts, while the other, more local, ORDER OF EVENTS and MAIN-SUBORDINATE constraints tend to be associated to a greater or lesser degree with the less formal, more oral type of writing. It is concluded that text evidence is directly relevant to an understanding of language processing.