Unimportant Words Research Articles

Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

<span lang="EN-US">Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.</span>

TAMNR: a network embedding learning algorithm using text attention mechanism.

Because many existing algorithms are mainly trained based on the structural features of the networks, the results are more inclined to the structural commonality of the networks. These algorithms ignore the rich external information and node attributes (such as node text content, community and labels, etc.) that have important implications for network data analysis tasks. Existing network embedding algorithms considering text features usually regard the co-occurrence words in the node's text, or use an induced matrix completion algorithm to factorize the text feature matrix or the network structure feature matrix. Although this kind of algorithm can greatly improve the network embedding performance, they ignore the contribution rate of different co-occurrence words in the node's text. This article proposes a network embedding learning algorithm combining network structure and co-occurrence word features, also incorporating an attention mechanism to model the weight information of the co-occurrence words in the model. This mechanism filters out unimportant words and focuses on important words for learning and training tasks, fully considering the impact of the different co-occurrence words to the model. The proposed network representation algorithm is tested on three open datasets, and the experimental results demonstrate its strong advantages in node classification, visualization analysis, and case analysis tasks.

Query-focused summarization with the context-graph information fusion transformer

Query-Focused Summarization (QFS) is a system that understands important information from a long document and generates them as a summary that can responds to the query. In QFS, how to properly utilize query information to generate a summary is a challenging problem. Existing Transformer-based QFS models, which is attending all words in the concatenation of a query and a document, result in inaccurate concentration on some unimportant words and they consequently cannot generate a good query-focused summary. This study proposes a Query-attentive Semantic Graph (QSG) that assists in identifying words related to the query, and a novel QFS model that generates a query-focused summary by appropriately fusing the contextual information of the language model with the structural information of QSG. In addition, we propose a novel personalized PageRank based graph neural network that computes each node’s importance score for the query inside QSG and utilizes it for node representation calculation. Experimental results on two QFS benchmarks show that the performance of the proposed model outperforms the simple Transformer-based model by large margins, as well as other state-of-the-art QFS.

Text Summarizing and Clustering Using Data Mining Technique

Text summarization is an important research topic in the field of information technology because of the large volume of texts, and the large amount of data found on the Internet and social media. The task of summarizing the text has gained great importance that requires finding highly efficient ways in the process of extracting knowledge in various fields, Thus, there was a need for methods of summarizing texts for one document or multiple documents. The summarization methods aim to obtain the main content of the set of documents at the same time to reduce redundant information. In this paper, an efficient method to summarize texts is proposed that depends on the word association algorithm to separate and merge sentences after summarizing them. As well as the use of data mining technology in the process of redistributing information according to the (K-Mean) algorithm and the use of (Term Frequency Inverse Document Frequency TF-IDF) technology for measuring the properties of summarized texts. The experimental results found that the summarization ratios are good by deleting unimportant words. Also, the method of extracting characteristics for texts was useful in grouping similar texts into clusters, which makes this method possible to be combined with other methods in artificial intelligence such as fuzzy logic or evolutionary algorithms in increasing summarization rates and accelerating cluster operations.

An Annotated Translation of Canvas

In this study, the writers discussed difficulties, solutions, and strategies used to translate poems of Canvas by doing an annotated translation. The data was taken from the book entitled ‘Canvas’ by Beni Wego. The strategies that the writers used were four strategies by Lefevere (1975), eight strategies by Baker (2018) and two strategies by Newmark (1988). Then, there were two most suitable strategies used which were translation by paraphrase using a related word and free translation. From the analysis, the difficulties were dealing with words that have similar meaning, difficult words to be translated in the TL, length of words, literal meaning, non-equivalent words, words that have multiple meanings, words that cannot be lexicalized, unimportant words, information, words that need more explanation, and idiom structure. From this study, it could be concluded that doing annotated translation help the process of finding out difficulties and solutions.

The So What of So in Writing Center Talk

Even small, taken-for- granted words can have a strong influence on the pedagogical effect of a writing conference. In this study, we examined how experienced and trained writing center tutors’ use of the discourse marker so helped them to connect ideas and to manage their conferences with students. We examined the extent to which tutors’ use of six types of so varied according to the English L1 (EL1)/ English L2 (EL2) status of their interlocutor. We studied 26 conferences: 13 involved eight tutors working with 13 EL1 students, and 13 conferences involved eight tutors working with 13 EL2 students. We found that conclusion/ result so occurred most frequently in tutors’ conferences with EL1 and EL2 students and that prompt so was the only type that exhibited a significant difference in frequency of occurrence between the two groups, occurring more frequently in tutors’ talk with EL1 students. We focused our qualitative analysis on prompt so, finding that it served two main purposes. We argue that examining discourse marker so generates implications for tutor training and shows the importance of paying attention to the small, seemingly unimportant words that tutors use.

Unsupervised Derivation of Keyword Summary for Short Texts

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy issues, e.g., personal information in the medical texts may be exposed. Moreover, compared with the complete short text where some unimportant words may exist, a summary consisting of only a few keywords is more preferable by the user due to its clear and concise form. Due to the above reasons, in this article, we aim to solve the problem of unsupervised derivation of keyword summary for short texts. Existing keyword extraction methods such as Latent Dirichlet Allocation cannot be applied to solve this problem, since (1) the ordering relations among the extracted keywords are ignored, which causes troubles for people to capture the main idea of the event, and (2) short texts contain limited context, which makes it hard to find the optimal words for semantic coverage. Hence, we propose a simple but yet effective method named Frequent Closed Wordsets Ranking (FCWRank) to derive the keyword summary from a short text cluster. FCWRank is an unsupervised method that builds on the idea of frequent closed itemset mining in transaction database. FCWRank first mines all frequent closed wordsets from a cluster of short texts and then selects the most important wordset based on an importance model where the similarity between closed wordsets and the relation between the closed wordset and the short text document are considered simultaneously. To make the keywords within the wordset more understandable, FCWRank further unfolds the semantics behind them by sorting them. Experiments on real-world short text collections show that FCWRank outperforms the state-of-the-art baselines in terms of Recall-Oriented Understudy for Gisting Evaluation-Longest common subsequence F1, precision and recall scores.

Pengaruh Phrase Detection dengan POS-Tagger terhadap Akurasi Klasifikasi Sentimen menggunakan SVM

Sentiment analysis or opinion mining, which is one of the application of Natural Language Processing (NLP), aims to find a method to facilitate human in communicating with a computer using their common language. To simplify the process of understanding human language, there are three important stages that must be carried out by a computer, which are tokenizing, stemming and filtering. The tokenizing that breaks down the sentence into a single word will make the computer assume all words (token) are the same. If there is a phrase formed from one of unimportant words, which is happened to be in the stoplist, the phrase will be deleted. Solution for the aforementioned problem is tokenizing based on phrase detection using Hidden Markov Model (HMM) POS-Tagger to improve classification performance using Support Vector Machine (SVM). With this approach, computer will be able to distinguish a phrase from others, then store the phrase into a single entity. There is an increase in accuracy by approximately 6% on Dataset I and 3% on Dataset II in the classification process using phrase detection, due to reduction of missing features that usually occurs in the filtering process. In addition, the detection of the phrase-based approach also produces the most optimal classification model, as seen from the ROC value that reaches 0.897.

Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval

This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks with Long Short-Term Memory (LSTM) cells. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detects the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms it for web document retrieval task.

Burst analysis for automatic concept map creation with a single document

Concept maps are graphical representations of knowledge that show important concepts as nodes and the relationships between the concepts as links. An automatically generated concept map, depicting the overview of a specific knowledge domain or documents, can facilitate the learner's understanding of the content. Constructing a good concept map from the given text requires correct selection of concept words and identification of meaningful relationships between the concepts. We propose a new method for generating the concept map from a single document on the basis of bursts of words and the temporal relationships between the bursts. Its strength is examined in comparison with the widely used co-occurrence-based approach. The proposed method differs in that it detects where a word becomes highly topical and the places where two words share such intense periods in the discourse indicating that they form a topic together. This is more advantageous for grasping the flow of a story than the co-occurrence method that simply relates frequently accompanying words throughout the text. Also the method is less affected by high frequency words so that more meaningful relationships can be included in the top links that are limitedly accommodated in a map. For sections in a document, the burst analysis is separately performed for the entire document and for each section not to miss the background knowledge or the foreground relationships that are specific to the part. When evaluated against the human-created concept map, the proposed method shows comparable or better performance than the co-occurrence method in the recall measure and more robustness to frequent unimportant words.

The Fractal Patterns of Words in a Text: A Method for Automatic Keyword Extraction.

A text can be considered as a one dimensional array of words. The locations of each word type in this array form a fractal pattern with certain fractal dimension. We observe that important words responsible for conveying the meaning of a text have dimensions considerably different from one, while the fractal dimensions of unimportant words are close to one. We introduce an index quantifying the importance of the words in a given text using their fractal dimensions and then ranking them according to their importance. This index measures the difference between the fractal pattern of a word in the original text relative to a shuffled version. Because the shuffled text is meaningless (i.e., words have no importance), the difference between the original and shuffled text can be used to ascertain degree of fractality. The degree of fractality may be used for automatic keyword detection. Words with the degree of fractality higher than a threshold value are assumed to be the retrieved keywords of the text. We measure the efficiency of our method for keywords extraction, making a comparison between our proposed method and two other well-known methods of automatic keyword extraction.

The Composition Process

As far as I can gather, the writing of a poem starts with a word or a phrase or a line or a vision. Which one immediately attempts to suppress because the ensuing processes range from that initial minor annoyance (rather like a thorn in the seat of the trousers) to very hard work indeed involving the making and breaking of phrases, agonising decisions on the selection of what are probably totally unimportant words, obsessive writings and rewritings to cut down as closely as possible to the so-called 'truth,' which being interpreted is: saying exactly what it is one is trying to convey.

“Without the clutter of unimportant words”

Keyphrases aid the exploration of text collections by communicating salient aspects of documents and are often used to create effective visualizations of text. While prior work in HCI and visualization has proposed a variety of ways of presenting keyphrases, less attention has been paid to selecting the best descriptive terms. In this article, we investigate the statistical and linguistic properties of keyphrases chosen by human judges and determine which features are most predictive of high-quality descriptive phrases. Based on 5,611 responses from 69 graduate students describing a corpus of dissertation abstracts, we analyze characteristics of human-generated keyphrases, including phrase length, commonness, position, and part of speech. Next, we systematically assess the contribution of each feature within statistical models of keyphrase quality. We then introduce a method for grouping similar terms and varying the specificity of displayed phrases so that applications can select phrases dynamically based on the available screen space and current context of interaction. Precision-recall measures find that our technique generates keyphrases that match those selected by human judges. Crowdsourced ratings of tag cloud visualizations rank our approach above other automatic techniques. Finally, we discuss the role of HCI methods in developing new algorithmic techniques suitable for user-facing applications.

The role of two reading strategies in text comprehension: An eye fixation study in primary school children

This study examined whether 10–12‐year‐old children use two reading strategies to aid their text comprehension: (1) distinguishing between important and unimportant words; and (2) resolving anaphoric references. Of interest was the question to what extent use of these reading strategies was predictive of reading comprehension skill over and above decoding skill and vocabulary. Reading strategy use was examined by the recording of eye fixations on specific target words. In contrast to less successful comprehenders, more successful comprehenders invested more processing time in important than in unimportant words. On the other hand, they needed less time to determine the antecedent of an anaphor. The results suggest that more successful comprehenders build a more effective mental model of the text than less successful comprehenders in at least two ways. First, they allocate more attention to the incorporation of goal‐relevant than goal‐irrelevant information into the model. Second, they ascertain that the text model is coherent and richly connected.

The Functional Value of the Participle in a Patent Text

The use of the Participle in a patent text is highly specific and makes it easily recognizable. On the one hand, the Participle plays a leading role between the main sentence and the participial clause, on the other hand, appearing in the main articles of the closing passage of the patent text, it aims to introduce and discuss the new crucial features of the invention avoiding unimportant words. It is also a bearer of theme-rheme relations in the text of the patent.

Giving and Loving

Giving and Loving

The discovery of a miniature linguistic system: Function words and comprehension of an unfamiliar language

Two experiments investigated two complementary hypotheses: (a) The presence of semantically unimportant function words in sentences will not slow down the way subjects, unfamiliar with the language, extract the meaning of those sentences, provided suprasegmental cues are present, and conversely (b) the omission of such function words—as in native speakers' simplified speech to nonnative speakers—will not necessarily facilitate the task of meaning extraction, contrary to popular belief. In Experiment I, Dutch adult subjects were exposed to sentences in a miniature artificial language (MAL). In Experiment II, Greek adult subjects were exposed to sentences in an unfamiliar natural language (Dutch). In both experiments, the sentences contained eight words constituting a miniature linguistic system (mls), a system that subjects discovered through an audiovisual, concept-indentification task. The rate by which subjects performed this task was investigated as a function of two linguistic variables: (a) the presence or absence of semantically unimportant function words, and (b) the presence or absence of suprasegmental cues, in the input sentences, respectively. Results from both experiments confirmed the hypotheses (p<.001) The findings are discussed with reference to the linguistic versus cognitive simplicity issue, and to the comprehension versus production issue in language acquisition research. The methodological potential of the experimental paradigm for psycholinguistic research is also discussed.

How to write good like a logistician should

This humorous article illustrates the major faults in present day communication and how to avoid them. Its centre theme is that reports contain many unnecessary words which force the reader to separate the important from the unimportant words until he becomes bored. The unnecessary words result from using indirect expressions, circumlocutions, the passive voice, and redundancy. This article gives examples of these unneccessary word generators and suggests how to remove them. It also gives other guidelines to improve writing.

Eye Movements in Reading: Facts and Fallacies

The daughter of a friend of mine, a junior in high school, came home the other day with the news that she and her classmates should be reading no slower than 750 words per minute in their literature books. Her English teacher said this was no trick at all-it merely required leaving out all the unimportant words and taking in large groups of words or phrases at each eye stop. The better students, according to the English teacher, would have no difficulty reading 1,500 words per minute. Jane and her classmates swallowed this hook, line, and sinker because the teacher had said it. The next day they started phrase-reading exercises. As the teacher tapped rhythmically on the desk, the students tried to make three eye stops on each line of print. This teacher would not have confused her students and wasted their time in this meaningless activity if she had had a better understanding of how the eyes and the mind work together in reading, how much (or how little) it is possible to control eye movements, and how eye-movement patterns and reading efficiency are related. However, she is not alone in being misinformed about the mechanics of reading. Many professional texts, reading-improvement workbooks, and popular articles on reading contain misconceptions about the actual performance of reading. A survey of 20 of the more widely distributed reading-improvement books and workbooks showed that 18 included statments about the visual-functional and perceptual acts that are in direct contradiction with research findings. The purpose of this article is to explore facts and fallacies related to eye movements and reading and to answer certain basic questions about the activity of reading.