Digital Media Hotspot Mining Algorithm Implementation with Complex Systems in the Mobile Internet Environment

With the development of the Internet, the amount of information present on the network has grown rapidly, leading to increased difficulty in obtaining effective information. Especially for individuals, enterprises, and institutions with a large amount of information, it is an almost impossible task to integrate and analyze Internet information with great difficulty just by human resources. Internet hot events mining and analysis technology can effectively solve the above problems by alleviating information overload, integrating redundant information, and refining core information. In this paper, we address the above problems and research hot event topic sentence generation techniques in the field of hot event mining and design a hybrid event candidate set construction algorithm based on topic core word mapping and event triad selection. The algorithm uses the PAT-Tree technique to extract high-frequency core words in topic hotspots and maps the high-frequency words into sentences to generate a part of event core sentences. The other part of event core sentences is extracted from the topic hotspots by making event triples as candidate elements, and sentences containing event elements are extracted from the topic hotspots. The sets of event core sentences generated by the two methods are mixed and filtered and sorted to obtain the candidate set, which can be used to build a word graph-based main service channel (MSC) model. In this paper, we also propose an improved word graph-based MSC model and use it for the extraction of event topic sentences. Based on the above research, a hot event analysis system is implemented. The system analyzes the existing topic data and uses the event topic sentence generation algorithm studied in this paper to generate the titles of hot spots, that is, hot events. At the same time, the topics are displayed from different dimensions, and data visualization is completed. The visualization includes the trend change of event hotness, trend change of event sentiment polarity, and distribution of event article sources.

Die Präposition „auf” als Verkettungskomponente

The article presents the idea of examining the preposition auf ‘on’ from the generative perspective, in which the preposition is understood as an elementary unit of the mental lexicon (lexical array) without a syntactic category. The unit auf ‘on’ is treated as a phonological segment to which a corresponding syntactic category is assigned in the selected syntactic context. The syntactic processing system ensures the correct assignment through correct decoding from auf ‘on’ by concatenating various grammatical features with different functions and meanings. The article analyses the unit auf ‘on’ in terms of concatenation properties in the syntactic process of sentence generation in German.

Language and Reasoning by Entropy Fractals

Like many other brain productions, language is a complex tool that helps individuals to communicate with each other. Many studies from computational linguistics aim to exhibit and understand the structures and content production. At present, a large list of contributions can describe and manage it with different levels of precision and applicability, but there is still a requirement for generative purposes. This paper is focused on stating the roots to understand language production from a combination of entropy and fractals. It is part of a larger work on seven rules that are intended to help build sentences automatically, in the context of dialogs with humans. As part of the scope of this paper, a set of dialogs are outlined and pre-processed. Three of the thermodynamic rules of language production are introduced and applied. Also, the communication implications and statistical evaluation are presented. From the results, a final analysis suggests that the exploration of fractals explanations of the entropy and entropy perspectives could provide a prospective insight for automatic sentence generation in natural language.

The language network supports both lexical access and sentence generation during language production

A network of left frontal and temporal brain regions has long been implicated in language comprehension and production. However, because of relatively fewer investigations of language production, the precise role of this ‘language network’ in production-related cognitive processes remains debated. Across four fMRI experiments that use picture naming/description to mimic the translation of conceptual representations into words and sentences, we characterize the response of the language regions to production demands. In line with prior studies, sentence production elicited strong responses throughout the language network. Further, we report three novel results. First, we demonstrate that production-related responses in the language network are robust to output modality (speaking vs. typing). Second, the language regions respond to both lexical access and sentence-generation demands. This pattern implies strong integration between lexico-semantic and combinatorial processes, mirroring the picture that has emerged in language comprehension. Finally, some have previously hypothesized the existence of production-selective mechanisms given that syntactic encoding is a critical part of sentence production, whereas comprehension is possible even when syntactic cues are degraded or absent. Contrary to this hypothesis, we find no evidence of brain regions that selectively support sentence generation. Instead, language regions respond overall more strongly during production than during comprehension, which suggests that production incurs a greater cost for the language network. Together, these results align with the idea that language comprehension and production draw on the same knowledge representations, which are stored in the language-selective network and are used both to interpret linguistic input and generate linguistic output.

A Comprehensive Study on Intelligence System for Automatize Event Tracker System Using Learning Method

In image processing, the radical scheme is required to propose a model for extracting the required content from an image. It plays a critical position to offer significant facts and needs methods in various automation arenas. By keeping the way of a parting textual content from images has proposed via following the sparse matrix illustration, grouping text components are based on heuristic rules and clustered into sentence generation. This paper directs a study on image analysis that inspects visual items as objects and different text patterns. Logistic Regression, Linear Discriminant Analysis naïve Bayes Algorithm are used to predict the image forms. This proposed work promotes the learning algorithm called Learning Vector Quantization Prediction Algorithm (LVQ Predict) is used to analysis the parts of the image. The features are extracted and classifies into printed and non-printed texts. Further, these texts are normalized and documented.

Sentence Generation for Indian Sign Language Using NLP

Nowadays the interaction among deaf and mute people and normal people is difficult, because normal people scuffle to understand the sense of the gestures. The deaf and dumb people find problem in sentence formation and grammatical correction. To alleviate the issues faced by these people, an automatic sign language sentence generation approach is propounded. In this project, Natural Language Processing (NLP) based methods are used. NLP is a powerful tool for translation in the human language and also responsible for the formation of meaningful sentences from sign language symbols which is also understood by the normal person. In this system, both conventional NLP methods and Deep learning NLP methods are used for sentence generation. The efficiency of both the methods are compared. The generated sentence is displayed in the android application as an output. This system aims to connect the gap in the interaction among the deaf and dumb people and the normal people.