Word Removal Research Articles

Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1

Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively.

LiHiSTO: a comprehensive list of Hindi stopwords

A preliminary preprocessing step in text analytics is the removal of words with no semantic meaning, otherwise known as stopwords. English stopwords are very easily accessible and created due to the broad usability of the English language. However, a standard list of Hindi stopwords is still missing. This paper proposes an exhaustive list of generic Hindi stopwords and a Python package for easy distribution and usage. The methodology uses a dual mechanism for creating a list of Hindi stopwords. First, the famous English stopwords are collected and translated into meaningful Hindi words (group 1). Second, unique Hindi stopwords from multiple sources are fetched (group 2). Finally, the respective Hindi stopwords from groups 1 and 2 are combined, which resulted in a significantly large set of 820 Hindi stopwords. Additionally, the list of Hindi stopwords is made openly available for use at the Python Package Index (PyPI) repository as a Python package, which is named LiHiSTO. With the help of illustrative implementations, it is shown that LiHiSTO provides abstract and easy access to the list of stopwords for users to perform Hindi text analytics.

Toxic Word Analyzer

In this generation social media has been a huge part of our lives and there is no need to say that the current generation spend a huge amount of time on their social media accounts. Apart from there being a good social media influencer there are a lot of people who spread hatred among these influencers as well as among each other. I have tried to make a speedometer which would be able to tell the toxicity of the words that are basically used in the input sentence or paragraph. The main processing that would be done on the sentence or the paragraph would be removing punctuation marks, tokenization on the words, ‘Stop’ word removal, bigram creation, matching tokens with predefined dictionary, generating toxicity percent using scaling.

Ellipsis in English and Albanian

This research attempted to conduct an in-depth analysis of the text-forming elements based on the fact that cohesive devices are insufficiently treated in the Albanian language, although considerable research and publications have been made in other languages. This study aims to bring evidence in recognizing, determining, and categorizing the structures of ellipsis and substitution which perform in English and Albanian. Comparing these important elements of grammatical cohesion in two languages will bring light upon the differences and similarities between the two languages. It will also show how frequently they are used in English and Albanian. Particularly, the aim is to show how these two mechanisms enable the avoidance of repetition, either by choosing other short words, phrases, and clauses or by removal of words, phrases, and clauses. This study involved samples of fiction and non-fiction texts of English and Albanian language, consisting of two novels and two daily newspapers. The findings of the research indicate that in fiction texts, ellipsis is used more in the Albanian language rather than in English whereas substitution prevails more in English than Albanian. Ellipsis is used more in the Albanian language rather than in English in non-fiction texts too. Nevertheless, the frequency of substitution seems to be the same in both languages with a total of 4 items in English and 3 items in Albanian.

Improvement of Conversation Skills through Script Fading in a Child with Autism Spectrum Disorder

The script fading procedure aims to develop better communication skills of children and youngsters with Autism Spectrum Disorder in relationship with other people. The procedure seeks to incorporate cues in several contexts. Scripts may be organized in day-to-day situations of life, such as playful interactions among children and the scripts may consist in pictorial, textual or auditory stimuli. If a learner, for example, lacks conversation skills, but has good repertoire regarding textual behavior, scripts consisting of written phrases may be presented and the learner has to read them. The listener may interact with the speaker like he is emitting responses free from the control of scripts. With repetitive exposure to the contingency and the establishment of a more fluent repertoire of textual behavior, the script is gradually faded out by word removal one by one. This process continues until the learner’s responses become free from the control of the whole scripts. Previous research has demonstrated the establishment of independent performance, maintenance and generalization to different settings and listeners. The purpose of this study was to assess the efficacy of this procedure in establishing conversation skills in a child aged 9 with ASD. Textual scripts were applied to three sets of conversation. After a baseline condition, training started and the experimental control by the independent variable was established through a multiple baseline design across the three different conversation topics. Independent performance was successfully established through reinforcement of correct performance and error correction. Although the interventions were conducted in a university-based laboratory, the established repertoires generalized to the child’s school classroom and another listeners (another adult and a child). Implications to school inclusion regarding the establishment of conversation among children in school setting were discussed.

Laparoscopic Hemi-Hysterectomy-Retrospective Study of Case Series of over 9 Years

Background: Word hemi-hysterectomy and removal of rudimentary functional horn may be used interchangeably in published data. The same term may be used when a non-obstructive hemi-uterus is removed when there is an associated pathology. The article tries to standardise classification of Mullerian dysgenesis where this procedure is required according to ESHRE ESGE classification, preoperative diagnosis and discuss the operative details. Objective: The objective was to study the demographic profile, symptoms, association of endometriosis, variation in the anatomy, accuracy of preoperative diagnosis, to classify according to ESHRE ESGE classification and to standardize the laparoscopic surgical steps of hemi-hysterectomy. Study Design: This is a retrospective case series of cases of Mullerian dysgenesis with obstructive hemi-uterus or non-obstructive hemi-uterus with pathology treated by laparoscopic hemi-hysterectomy. (Canadian task force classification III). Methods: Data from hospital electronic records of all cases tagged with word laparoscopic hemi-hysterectomy were collected for 9 years from Jan 2009 to Dec 2018. Results: Total 19 patients of hemi-hysterectomy were analysed. Pre-operative diagnosis was made in 100% of patients. 100% patients with obstructive horn had dysmenorrhoea. ESHRE ESGE class U4aC3V0 was more frequently seen followed by U5aC4V4 and U3bC3V2 as obstructive and U4bC3V0, as non-obstructive. Associated endometriosis along with other pathology was seen in 74% of the patients. 3 patients with HWWS had ipsilateral renal agenesis. Laparoscopic hemi-hysterectomy was offered to all such patients. The operative steps & variants were studied. Post-operative outcome was uneventful in all. Conclusion: An experienced surgeon should deal with these problems of Mullerian dysgenesis discussed in this study. The other variants diagnosed also must be treated by an experienced surgeon only. Laparoscopic hemi-hysterectomy is almost the most standard surgical method to treat pelvic pain in cases with rudimentary non-communicating horns or in cases of non-obstructive horns with other associated pathologies. Post-operative recovery is uneventful, and all patients reported pain free periods as well as they are free of chronic pain which they had before surgery.

KOHESI GRAMATIKAL: KAJIAN KEUTUHAN WACANA TUGAS MAHASISWA PENDIDIKAN BAHASA DAN SASTRA INDONESIA, FAKULTAS BAHASA DAN SASTRA, UNIVERSITAS NEGERI MAKASSAR

Abstract : This study aims to elaborate on the use of grammatical cohesion student assignment. The research method is content analysis. Data source is the text contained in the student assignment Indonesian Language and Literature Education. The data in the form of grammatical cohesion. Data analysed with the method content analysis. The results showed that: first , the pronouns. The use of pronouns we and they are often used in stringing sentences students conducted again. Second , the substitution. Substitution that occurs in the tasks students are generally limited to the replacement of the word or group of words with pronouns. Third , the elepsis. Elepsis in student assignments sometimes occur due to the removal of appropriate words must be present in the sentence. Fourth , conjunctions. Conjunctions are used in student writing right there and some are not appropriate. This is caused by a lack of understanding of student use of conjunctions Permalink/DOI: dx.doi.org/10.17977/um015v45i22017p170

Discovering Semantic and Sentiment Correlations using Short Informal Arabic Language Text

Semantic and Sentiment analysis have received a great deal of attention over the last few years due to the important role they play in many different fields, including marketing, education, and politics. Social media has given tremendous opportunities for researchers to collect huge amount of data as input for their semantic and sentiment analysis. Using twitter API, we collected around 4.5 million Arabic tweets and used them to propose a novel automatic unsupervised approach to capture patterns of words and sentences of similar contextual semantics and sentiment in informal Arabic language at word and sentence levels. We used Language Modeling (LM) model which is statistical model that can estimate the distribution of natural language in effective way. The results of experiments of proposed model showed better performance than classic bigram and latent semantic analysis (LSA) model in most of cases at word level. In order to handle the big data, we used different text processing techniques followed by removal of the unique words based on their rele Informal Arabic, Big Data, Sentiment analysis, Opinion Mining (OM), semantic analysis, bigram model, LSA model, Twitter vance to problem.

Development of a computer system for generating semantic template of a group of documents by using latent semantic analysis

The CS was developed by means of the Python programming language to generate a semantic template of a group of documents by the LSA method. The system contains eight software modules, each performs one stage of the LSA. The control module of the frequency word-document matrix and the measuring module of semantic distance between the template documents are unique. Adjustment of CS to the contents and structure of the document templates is performed by changing a set of modules. According to the research, the frequency matrix normalization enhances the resolution of the semantic template generated by using the LSA. It is proved that the removal of individual words improves the resolution of the generated semantic template and does not affect the semantic content. Application of semantic proximity of documents, the cosine of the difference of angles between the vector of a group of basic words and vectors of documents for evaluation allows increasing the resolution of the generated semantic template. To ensure the continuity of the LSA, the module of the frequency matrix analysis for compliance of excess (or equality) of the number of words over the number of documents was introduced in the CS. In the event of a mismatch, the module starts over the LSA process with a new set of words and documents after removal of the inappropriate document and related words.

Willow: a system to automatically assess students' free-text answers by using a combination of shallow NLP techniques

In this paper, a new approach to automatically assessing students’ short free-text answers is presented. It has been implemented into the multilingual web-based application called Willow. Willow is able to formulate a question (chosen randomly or according to the students’ personal features) and, to score the answer, written in Spanish or in English, by using a combination of shallow NLP techniques such as stemming, removal of closed-class words, word sense disambiguation or multiword term identification. Several experiments have been carried out to identify the best combination of techniques for each language to find the optimal configuration in each case.

Using corpus statistics to remove redundant words in text categorization

This article studies aggressive word removal in text categorization to reduce the noise in free texts and to enhance the computational efficiency of categorization. We use a novel stop word identification method to automatically generate domain specific stoplists which are much larger than a conventional domain-independent stoplist. In our tests with three categorization methods on text collections from different domains/applications, significant numbers of words were removed without sacrificing categorization effectiveness. In the test of the Expert Network method on CACM documents, for example, an 87% removal of unique words reduced the vocabulary of documents from 8,002 distinct words to 1,045 words, which resulted in a 63% time savings and a 74% memory savings in the computation of category ranking, with a 10% precision improvement on average over not using word removal. It is evident in this study that automated word removal based on corpus statistics has a practical and significant impact on the computational tractability of categorization methods in large databases.