Search results for: unstructured text

Authors: K. P. Sandesh, M. H. Suman

Abstract:

Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures.

Keywords: document classification, document clustering, entropy, accuracy, classifiers, clustering algorithms

Procedia PDF Downloads 480

Authors: Siti Azrina B. A. Aziz, Siti Hafizah A. Hamid

Abstract:

New approaches to analyze and visualize data stream in real-time basis is important in making a prompt decision by the decision maker. Financial market trading and surveillance, large-scale emergency response and crowd control are some example scenarios that require real-time analytic and data visualization. This situation has led to the development of techniques and tools that support humans in analyzing the source data. With the emergence of Big Data and social media, new techniques and tools are required in order to process the streaming data. Today, ranges of tools which implement some of these functionalities are available. In this paper, we present chronological evolution evaluation of technologies for supporting of real-time analytic and visualization of the data stream. Based on the past research papers published from 2002 to 2014, we gathered the general information, main techniques, challenges and open issues. The techniques for streaming text visualization are identified based on Text Visualization Browser in chronological order. This paper aims to review the evolution of streaming text visualization techniques and tools, as well as to discuss the problems and challenges for each of identified tools.

Keywords: information visualization, visual analytics, text mining, visual text analytics tools, big data visualization

Procedia PDF Downloads 373

Authors: Tadesse Anberbir, Bankole Felix, Tomio Takara

Abstract:

In the development of a text-to-speech synthesizer, automatic derivation of correct pronunciation from the grapheme form of a text is a central problem. Particularly deriving phonological features which are not shown in orthography is challenging. In the Amharic language, geminates and epenthetic vowels are very crucial for proper pronunciation, but neither is shown in orthography. In this paper, to proposed and integrated a morphological analyzer into an Amharic Text-to-Speech system, mainly to predict geminates and epenthetic vowel positions and prepared a duration modeling method. Amharic Text-to-Speech system (AmhTTS) is a parametric and rule-based system that adopts a cepstral method and uses a source filter model for speech production and a Log Magnitude Approximation (LMA) filter as the vocal tract filter. The naturalness of the system after employing the duration modeling was evaluated by sentence listening test, and we achieved an average Mean Opinion Score (MOS) 3.4 (68%), which is moderate. By modeling the duration of geminates and controlling the locations of epenthetic vowel, we are able to synthesize good quality speech. Our system is mainly suitable to be customized for other Ethiopian languages with limited resources.

Keywords: amharic, gemination, Speech synthesis, morphology, epenthesis

Procedia PDF Downloads 55

Authors: Trisha Malhotra

Abstract:

Sentiment analysis is a recent field of study that computationally assesses the emotional nature of a body of text. To assess its test-validity, sentiment analysis was carried out on the emotional corpus of text from a personal 15-day mood diary. Self-reported mood scores varied more or less accurately with daily mood evaluation score given by the software. On further assessment, it was found that while sentiment analysis was good at assessing ‘global’ mood, it was not able to ‘locally’ identify and differentially score synonyms of various emotional words. It is further critiqued for treating the intensity of an emotion as universal across cultures. Finally, the software is shown not to account for emotional complexity in sentences by treating emotions as strictly positive or negative. Hence, it is posited that a better output could be two (positive and negative) affect scores for the same body of text.

Keywords: analysis, data, diary, emotions, mood, sentiment

Procedia PDF Downloads 238

Authors: Azyz Sharafy

Abstract:

3D Text Toys is an innovative and creative approach that utilizes 3D text objects to enhance creativity, literacy, and basic learning in an enjoyable and gamified manner. By using 3D Text Toys, children can develop their creativity, visually learn words and texts, and apply their artistic talents within their creative abilities. This process incorporates haptic engagement with 2D and 3D texts, word building, and mechanical construction of everyday objects, thereby facilitating better word and text retention. The concept involves constructing visual objects made entirely out of 3D text/words, where each component of the object represents a word or text element. For instance, a bird can be recreated using words or text shaped like its wings, beak, legs, head, and body, resulting in a 3D representation of the bird purely composed of text. This can serve as an art piece or a learning tool in the form of a 3D text toy. These 3D text objects or toys can be crafted using natural materials such as leaves, twigs, strings, or ropes, or they can be made from various physical materials using traditional crafting tools. Digital versions of these objects can be created using 2D or 3D software on devices like phones, laptops, iPads, or computers. To transform digital designs into physical objects, computerized machines such as CNC routers, laser cutters, and 3D printers can be utilized. Once the parts are printed or cut out, students can assemble the 3D texts by gluing them together, resulting in natural or everyday 3D text objects. These objects can be painted to create artistic pieces or text toys, and the addition of wheels can transform them into moving toys. One of the significant advantages of this visual and creative object-based learning process is that students not only learn words but also derive enjoyment from the process of creating, painting, and playing with these objects. The ownership and creation process further enhances comprehension and word retention. Moreover, for individuals with learning disabilities such as dyslexia, ADD (Attention Deficit Disorder), or other learning difficulties, the visual and haptic approach of 3D Text Toys can serve as an additional creative and personalized learning aid. The application of 3D Text Toys extends to both the English language and any other global written language. The adaptation and creative application may vary depending on the country, space, and native written language. Furthermore, the implementation of this visual and haptic learning tool can be tailored to teach foreign languages based on age level and comprehension requirements. In summary, this creative, haptic, and visual approach has the potential to serve as a global literacy tool.

Keywords: 3D text toys, creative, artistic, visual learning for world literacy

Procedia PDF Downloads 34

Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

Abstract:

In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: evolving learning, knowledge extraction, knowledge graph, text mining

Procedia PDF Downloads 434

Authors: Ibrahim Hassan

Abstract:

Motion typography is one of the most important types of visual communication based on display. Through the digital display media, we can control the text properties (size, direction, thickness, color, etc.). The use of motion typography in visual communication made it have several images. We need to adjust the terminology and clarify the different differences between them, so relying on the word motion typography -considered a general term- is not enough to separate the different communicative functions of the moving text. In this paper, we discuss the different effects of motion typography on Arabic writing and how we can achieve harmony between the movement and the letterform, and we will, during our experiments, present a new type of text movement.

Keywords: Arabic typography, motion typography, kinetic typography, fluid typography, temporal typography

Procedia PDF Downloads 121

Authors: Farshideh Einsele, Hassan Foroosh

Abstract:

In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using wellknown geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: camera-based OCR, feature extraction, document, image processing, grocery products

Procedia PDF Downloads 375

Authors: Zarine Avetisyan

Abstract:

Both in language and literature there exists the theory of recurrence of text and talk chunks which brings us to the notion of precedence. It must be stated that precedence as a pragma-linguistic phenomenon is yet underknown and it is the main objective of the present research to revisit and reveal it thoroughly. In line with the main research objective, analysis of political text and talk provides abundant relevant data for the illustration of the phenomenon of precedence. The analysis focuses on certain pragmatic universals (e.g. intention) and categories (e.g. speech techniques) which lead to the disclosure of the present object of study.

Keywords: intention, precedence, political discourse, pragmatic universals

Procedia PDF Downloads 396

Authors: Tadesse Anberbir, Felix Bankole, Tomio Takara, Girma Mamo

Abstract:

In the development of a text-to-speech synthesizer, automatic derivation of correct pronunciation from the grapheme form of a text is a central problem. Particularly deriving phonological features which are not shown in orthography is challenging. In the Amharic language, geminates and epenthetic vowels are very crucial for proper pronunciation but neither is shown in orthography. In this paper, we proposed and integrated a morphological analyzer into an Amharic Text-to-Speech system, mainly to predict geminates and epenthetic vowel positions, and prepared a duration modeling method. Amharic Text-to-Speech system (AmhTTS) is a parametric and rule-based system that adopts a cepstral method and uses a source filter model for speech production and a Log Magnitude Approximation (LMA) filter as the vocal tract filter. The naturalness of the system after employing the duration modeling was evaluated by sentence listening test and we achieved an average Mean Opinion Score (MOS) 3.4 (68%) which is moderate. By modeling the duration of geminates and controlling the locations of epenthetic vowel, we are able to synthesize good quality speech. Our system is mainly suitable to be customized for other Ethiopian languages with limited resources.

Keywords: Amharic, gemination, speech synthesis, morphology, epenthesis

Procedia PDF Downloads 49

Authors: Archit Yajnik

Abstract:

Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: hidden markov model, natural language processing, POS tagging, viterbi algorithm

Procedia PDF Downloads 302

Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

Abstract:

We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

Procedia PDF Downloads 40

Authors: Afia Shahid

Abstract:

The writing of Samuel Beckett is associated with meaning in the meaninglessness and the production of what he calls ‘literature of unword’. The casual escape from the world of words in the form of silences and pauses, in his play Waiting for Godot, urges to ask question of their existence and ultimately leads to investigate the theory behind their use in the play. This paper proposes that these absences (silence and pause) in Beckett’s play force to think ‘beyond’ language. This paper asks how silence and pause in Beckett’s text speak for the emergence of poststructuralist text. It aims to identify the significant features of the philosophy of deconstruction in the play of Beckett to demystify the hostile complicity between literature and philosophy. With the interpretive paradigm of poststructuralism this research focuses on the text as a research data. It attempts to delineate the relationship between poststructuralist theoretical concerns and text of Beckett. Keeping in view the theoretical concerns of Poststructuralist theorist Jacques Derrida, the main concern of the discussion is directed towards the notion of ‘beyond’ language into the absences that are aimed at silencing the existing discourse with the ‘radical irony’ of this anti-formal art that contains its own denial and thus represents the idea of ceaseless questioning and radical contradiction in art and any text. This article asks how text of Beckett vibrates with loud silence and has disrupted language to demonstrate the emptiness of words and thus exploring the limitless void of absences. Beckett’s text resonates with silence and pause that is neither negation nor affirmation rather a poststructuralist’s suspension of reality that is ever changing with the undecidablity of all meanings. Within the theoretical notion of Derrida’s Différance this study interprets silence and pause in Beckett’s art. The silence and pause behave like Derrida’s Différance and have questioned their own existence in the text to deconstruct any definiteness and finality of reality to extend an undecidable threshold of poststructuralists that aims to evade the ‘labyrinth of language’.

Keywords: Différance, language, pause, poststructuralism, silence, text

Procedia PDF Downloads 177

Authors: Erekle Magradze, Davit Soselia, Levan Shughliashvili, Irakli Koberidze, Shota Tsiskaridze, Victor Kakhniashvili, Tamar Chaghiashvili

Abstract:

Since the beginning of active publishing activity in Georgia, voluminous printed material has been accumulated, the digitization of which is an important task. Digitized materials will be available to the audience, and it will be possible to find text in them and conduct various factual research. Digitizing scanned documents means scanning documents, extracting text from the scanned documents, and processing the text into a corresponding language model to detect inaccuracies and grammatical errors. Implementing these stages requires a unified, scalable, and automated platform, where the digital service developed for each stage will perform the task assigned to it; at the same time, it will be possible to develop these services dynamically so that there is no interruption in the work of the platform.

Keywords: NLP, OCR, BERT, Kubernetes, transformers

Procedia PDF Downloads 114

Authors: Toktam Khatibi

Abstract:

Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

Procedia PDF Downloads 45

Authors: Prashanth Pillai, Purnaprajna Mangsuli

Abstract:

Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

Procedia PDF Downloads 166

Authors: Gemma García López

Abstract:

The goal of this study is to analyze if search queries carried out in search engines such as Google, can offer emotional information about the user that performs them. Knowing the emotional state in which the Internet user is located can be a key to achieve the maximum personalization of content and the detection of worrying behaviors. For this, two studies were carried out using tools with advanced natural language processing techniques. The first study determines if a query can be classified as positive, negative or neutral, while the second study extracts emotional content from words and applies the categorical and dimensional models for the representation of emotions. In addition, we use search queries in Spanish and English to establish similarities and differences between two languages. The results revealed that text search queries performed by users on the Internet can be classified emotionally. This allows us to better understand the emotional state of the user at the time of the search, which could involve adapting the technology and personalizing the responses to different emotional states.

Keywords: emotion classification, text search queries, emotional analysis, sentiment analysis in text, natural language processing

Procedia PDF Downloads 115

Authors: Ichihan Tai, Bill Olson, Paul Blessner

Abstract:

Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

Procedia PDF Downloads 300

Authors: Iftikhar A. Tayubi, Aabdulrahman Alsubhi, Abdullah Althrwi

Abstract:

The deoxyribonucleic acid text provides a single source of high-quality Cryptography about Deoxyribonucleic acid sequence for structural biologists. We will provide an intuitive, well-organized and user-friendly web interface that allows users to encrypt and decrypt Deoxy Ribonucleic Acid sequence text. It includes complex, securing by using Algorithm to encrypt and decrypt Deoxy Ribonucleic Acid sequence. The utility of this Deoxy Ribonucleic Acid Sequence Text is that, it can provide a user-friendly interface for users to Encrypt and Decrypt store the information about Deoxy Ribonucleic Acid sequence. These interfaces created in this project will satisfy the demands of the scientific community by providing fully encrypt of Deoxy Ribonucleic Acid sequence during this website. We have adopted a methodology by using C# and Active Server Page.NET for programming which is smart and secure. Deoxy Ribonucleic Acid sequence text is a wonderful piece of equipment for encrypting large quantities of data, efficiently. The users can thus navigate from one encoding and store orange text, depending on the field for user’s interest. Algorithm classification allows a user to Protect the deoxy ribonucleic acid sequence from change, whether an alteration or error occurred during the Deoxy Ribonucleic Acid sequence data transfer. It will check the integrity of the Deoxy Ribonucleic Acid sequence data during the access.

Keywords: algorithm, ASP.NET, DNA, encrypt, decrypt

Procedia PDF Downloads 202

Authors: Narges Farokhshad, Milad Molazadeh, Saman Jamalabbasi, Hamed Babaei Giglou, Saeed Bibak

Abstract:

The Persian language is an inflectional subject-object-verb language. This fact makes Persian a more uncertain language. However, using techniques such as Zero-Width Non-Joiner (ZWNJ) recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most of the works in Persian, these techniques are addressed individually. Despite that, we believe that for text refinement in Persian, all of these tasks are necessary. In this work, we proposed a ViraPart framework that uses embedded ParsBERT in its core for text clarifications. First, used the BERT variant for Persian followed by a classifier layer for classification procedures. Next, we combined models outputs to output cleartext. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90%, 92.13%, and 98.50%, respectively. Experimental results show that our proposed approach is very effective in text refinement for the Persian language.

Keywords: Persian Ezafe, punctuation, ZWNJ, NLP, ParsBERT, transformers

Procedia PDF Downloads 171

Authors: Snezana Savoska, Blagoj Ristevski, Violeta Manevska, Zlatko Savoski, Ilija Jolevski

Abstract:

Big data is widely accepted by the pharmaceutical companies as a result of business demands create through legal pressure. Pharmaceutical companies have many legal demands as well as standards’ demands and have to adapt their procedures to the legislation. To manage with these demands, they have to standardize the usage of the current information technology and use the latest software tools. This paper highlights some important aspects of experience with big data projects implementation in a pharmaceutical Macedonian company. These projects made improvements of their business processes by the help of new software tools selected to comply with legal and business demands. They use IT as a strategic tool to obtain competitive advantage on the market and to reengineer the processes towards new Internet economy and quality demands. The company is required to manage vast amounts of structured as well as unstructured data. For these reasons, they implement projects for emerging and appropriate software tools which have to deal with big data concepts accepted in the company.

Keywords: big data, unstructured data, SAP ERP, documentum

Procedia PDF Downloads 235

Authors: Hamza A. Al-Sewadi, Akram N. A. Aldakari

Abstract:

Copyright protection and ownership proof of digital multimedia are achieved nowadays by digital watermarking techniques. A text watermarking algorithm for protecting the property rights and ownership judgment of color images is proposed in this paper. Embedding is achieved by inserting texts elements randomly into the color image as noise. The YIQ image processing model is found to be faster than other image processing methods, and hence, it is adopted for the embedding process. An optional choice of encrypting the text watermark before embedding is also suggested (in case required by some applications), where, the text can is encrypted using any enciphering technique adding more difficulty to hackers. Experiments resulted in embedding speed improvement of more than double the speed of other considered systems (such as least significant bit method, and separate color code methods), and a fairly acceptable level of peak signal to noise ratio (PSNR) with low mean square error values for watermarking purposes.

Keywords: steganography, watermarking, time complexity measurements, private keys

Procedia PDF Downloads 118

Authors: Abdul Rahman M. Chamseddine, Mahmoud El-ashiri

Abstract:

In the field of language teaching, communication skills are essential for the learner to achieve, however, these skills, in general, might not support the comprehension of some texts of literary or artistic nature like poetry. Understanding sentences and expressions is not enough to understand a poem; other skills are needed in order to understand the special structure of a text which literary meaning is inapprehensible even when the lingual meaning is well comprehended. And then there is the need for many other components that surpass one text to other similar texts that can be understood through solid traditions, which do not form an obstacle in the face of change and progress. This is not exclusive to texts that are classified as a literary but it is also the same with some daily short phrases and indicatively charged expressions that can be classified as literary or bear a taste of literary nature.. it can be found in Newpapers’ titles, TV news reports, and maybe football commentaries… the need to understand this special lingual use – described as literary – is highly important to understand this discourse that can be generally classified as very far from literature. This work will try to explore the role of the literary text in the language class and the way it is being covered or dealt with throughout all levels of acquiring proficiency. It will also attempt to survery the position of the literary text in some of the most important books for teaching Arabic around the world. The same way grammar is needed to understand the language, another (literary) grammar is also needed for understanding literature.

Keywords: language teaching, Arabic, literature, pedagogy, language proficiency

Procedia PDF Downloads 243

Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

Abstract:

The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

Procedia PDF Downloads 93

Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

Abstract:

Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining

Procedia PDF Downloads 276

Authors: Will Brown

Abstract:

Martin Heidegger is considered to be one of the leading philosophical lights of the 20th century with his lecture/essay 'The Question Concerning Technology' proving to be an invaluable text in the study of technology and the understanding of how technology influences the world it is set upon. However, this text has not as of yet been applied to the rapid rise and proliferation of ‘smart’ cities. This article is premised upon the application of the aforementioned text and the smart city in order to provide a fresh, if not critical analysis and interpretation of this phenomena. The first section below provides a brief literature review of smart urbanism in order to lay the groundwork necessary to apply Heidegger’s work to the smart city, from which a framework is developed to interpret the infusion of digital sensing technologies and the urban milieu. This framework is comprised of four concepts put forward in Heidegger’s text: circumscribing, bringing-forth, challenging, and standing-reserve. A concluding chapter is based upon the notion of enframement, arguing that once the rubric of data collection is placed within the urban system, future systems will require the capability to harvest data, resulting in an ever-renewing smart city.

Keywords: air quality sensing, big data, Martin Heidegger, smart city

Procedia PDF Downloads 176

Authors: Ivan Potapov

Abstract:

Background: The aim of this paper is to investigate poly code texts in the communication of youth antisocial groups. Nowadays, the notion of a text has numerous interpretations. Besides all the approaches to defining a text, we must take into account semiotic and cultural-semiotic ones. Rapidly developing IT, world globalization, and new ways of coding of information increase the role of the cultural-semiotic approach. However, the development of computer technologies leads also to changes in the text itself. Polycode texts play a more and more important role in the everyday communication of the younger generation. Therefore, the research of functional and pragmatic aspects of both verbal and non-verbal content is actually quite important. Methods and Material: For this survey, we applied the combination of four methods of text investigation: not only intention and content analysis but also semantic and syntactic analysis. Using these methods provided us with information on general text properties, the content of transmitted messages, and each communicants’ intentions. Besides, during our research, we figured out the social background; therefore, we could distinguish intertextual connections between certain types of polycode texts. As the sources of the research material, we used 20 public channels in the popular messenger Telegram and data extracted from smartphones, which belonged to arrested members of antisocial groups. Findings: This investigation let us assert that polycode texts can be characterized as highly intertextual language unit. Moreover, we could outline the classification of these texts based on communicants’ intentions. The most common types of antisocial polycode texts are a call to illegal actions and agitation. What is more, each type has its own semantic core: it depends on the sphere of communication. However, syntactic structure is universal for most of the polycode texts. Conclusion: Polycode texts play important role in online communication. The results of this investigation demonstrate that in some social groups using these texts has a destructive influence on the younger generation and obviously needs further researches.

Keywords: text, polycode text, internet linguistics, text analysis, context, semiotics, sociolinguistics

Procedia PDF Downloads 105

Authors: Dim En Nyaung, Thin Lai Lai Thein

Abstract:

Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

Procedia PDF Downloads 307

Authors: Khaled M. Alhawiti

Abstract:

This paper aims to analyze the role of natural language processing (NLP). The paper will discuss the role in the context of automated data retrieval, automated question answer, and text structuring. NLP techniques are gaining wider acceptance in real life applications and industrial concerns. There are various complexities involved in processing the text of natural language that could satisfy the need of decision makers. This paper begins with the description of the qualities of NLP practices. The paper then focuses on the challenges in natural language processing. The paper also discusses major techniques of NLP. The last section describes opportunities and challenges for future research.

Keywords: data retrieval, information retrieval, natural language processing, text structuring

Procedia PDF Downloads 312

Authors: Saleem Abu Jaber, Khaled Igbaria

Abstract:

For readers, space/place is one of the most significant references to reveal deep significances and indications in modern Arabic poetic texts. Generally, when poets evoke places and/or spaces, they do not mean to refer readers to detailed geographic or physical spaces, but to the symbolic significances and dimensions that those spaces have and through which poets encourage spacial awareness in their readers. Recently, as a result, there has been a great deal of interest in research addressing spacial poetic texts and dimensions in modern Arabic poetry in general and in Palestinian poetry in particular. Samih al-Qasim is one of the most recent prominent Palestinian revolutionary poets. Al-Qasim has published six series of poems that are well known in the Arab world. Although several researchers have studied al-Qasim's poetry, to our knowledge, yet no one has studied the aspect of spacial poetic text in his poetry. Therefore, this paper seeks to fill a gap in the scholarship that has not been addressed up to now. This article aims, not only to demonstrate the presence of spacial poetic text and dimensions throughout al-Qasim's poetry, but also to investigate the purpose for which the poet uses spacial poetic text. Our theory is that the poet, consciously and significantly, uses spacial poetic texts to magnify the Palestinian identity of the Palestinian readers.  Methodologically, we applied a descriptive analytic method, referencing al-Qasim's poetry, addressing spacial poetic texts practically but not theoretically or statistically.

Keywords: spatial poetic text, Samih al-Qasim, space and identity, Palestinian poetry

Procedia PDF Downloads 286