• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.40-56
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• 2022

This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package "rvest." The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. "LIS Recruitment," "LIS Issues," "Other Discussion," "LIS Education," "LIS Research," "LIS Exams," "General Information related to Library," "LIS Admission," "Library and Professional Activities," and "Information Communication Technology (ICT)." It was found that the majority of the posts belonged to "LIS Exam," followed by "Other Discussions" and "General Information related to the Library."

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.111-120
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• 2015

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.94-100
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• 2012

In this paper, we propose a system which improves text catchphrases on the web using onomatopoeia and the Japanese Google N-grams. Onomatopoeia is regarded as a fundamental tool in daily communication for people. The proposed system inserts an onomatopoetic word into plain text catchphrases. Being based on a large catchphrase encyclopedia, the proposed system evaluates each catchphrase's candidates considering the words, structure and usage of onomatopoeia. That is, candidates are selected whether they contain onomatopoeia and they use specific catchphrase grammatical structures. Subjective experiments show that inserted onomatopoeia is effective for making attractive catchphrases.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.285-292
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• 2007

The parallel corpus is an important resource in the research field of data-driven natural language processing, but there are only a few parallel corpora publicly available nowadays, mostly due to the high labor force needed to construct this kind of resource. A novel strategy is brought out to automatically fetch parallel text from the web in this paper, which may help to solve the problem of the lack of parallel corpora with high quality. The system we develop first downloads the web pages from certain hosts. Then candidate parallel page pairs are prepared from the page set based on the outer features of the web pages. The candidate page pairs are evaluated in the last step in which the sentences in the candidate web page pairs are extracted and aligned first, and then the similarity of the two web pages is evaluate based on the similarities of the aligned sentences. The experiments towards a multilingual web site show the satisfactory performance of the system.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.401-403
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• 2019

In this paper, we propose a generative adversarial networks (GAN) based text-to-image generating method. In many natural language processing tasks, which word expressions are determined by their term frequency -inverse document frequency scores. Word2Vec is a type of neural network model that, in the case of an unlabeled corpus, produces a vector that expresses semantics for words in the corpus and an image is generated by GAN training according to the obtained vector. Thanks to the understanding of the word we can generate higher and more realistic images. Our GAN structure is based on deep convolution neural networks and pixel recurrent neural networks. Comparing the generated image with the real image, we get about 88% similarity on the Oxford-102 flowers dataset.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.404-410
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• 2002

No Abstract(See Full Text)

• MALSORI
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• v.68
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• pp.115-126
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• 2008

In this paper, we propose a vocabulary coverage improvement method for embedded continuous speech recognition (CSR) using knowledgebase. A vocabulary in CSR is normally derived from a word frequency list. Therefore, the vocabulary coverage is dependent on a corpus. In the previous research, we presented an improved way of vocabulary generation using part-of-speech (POS) tagged corpus. We analyzed all words paired with 101 among 152 POS tags and decided on a set of words which have to be included in vocabularies of any size. However, for the other 51 POS tags (e.g. nouns, verbs), the vocabulary inclusion of words paired with such POS tags are still based on word frequency counted on a corpus. In this paper, we propose a corpus independent word inclusion method for noun-, verb-, and named entity(NE)-related POS tags using knowledgebase. For noun-related POS tags, we generate synonym groups and analyze their relative importance using Google search. Then, we categorize verbs by lemma and analyze relative importance of each lemma from a pre-analyzed statistic for verbs. We determine the inclusion order of NEs through Google search. The proposed method shows better coverage for the test short message service (SMS) text corpus.

• Asia Pacific Journal of Corpus Research
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• v.5 no.1
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• pp.23-36
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• 2024

This research centers on the Taiwan Eye-Movement Corpus of Spanish (TECS), a specially created corpus comprising eye-tracking data from Chinese-speaking learners of Spanish as a third language in Taiwan. Its primary purpose is to explore the broad utility of TECS in understanding language learning processes, particularly the initial stages of language learning. Constructing this corpus involves gathering data on eye-tracking, reading comprehension, and language proficiency to develop a machine-learning model that predicts learner behaviors, and subsequently undergoes a predictability test for validation. The focus is on examining attention in input processing and their relationship to language learning outcomes. The TECS eye-tracking data consists of indicators derived from eye movement recordings while reading Spanish sentences with temporal references. These indicators are obtained from eye movement experiments focusing on tense verbal inflections and temporal adverbs. Chinese expresses tense using aspect markers, lexical references, and contextual cues, differing significantly from inflectional languages like Spanish. Chinese-speaking learners of Spanish face particular challenges in learning verbal morphology and tenses. The data from eye movement experiments were structured into feature vectors, with learner behaviors serving as class labels. After categorizing the collected data, we used two types of machine learning methods for classification and regression: Random Forests and the k-nearest neighbors algorithm (KNN). By leveraging these algorithms, we predicted learner behaviors and conducted performance evaluations to enhance our understanding of the nexus between learner behaviors and language learning process. Future research may further enrich TECS by gathering data from subsequent eye-movement experiments, specifically targeting various Spanish tenses and temporal lexical references during text reading. These endeavors promise to broaden and refine the corpus, advancing our understanding of language processing.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.223-231
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• 2022

The application of computer software into the linguistic analysis of texts proves useful to arrive at concise and authentic results from large data texts. Based on this assumption, this paper employs a Computer-Aided Text Analysis (CATA) and a Critical Discourse Analysis (CDA) to explore the manipulative strategies of positive/negative presentation in Orwell's Animal Farm. More specifically, the paper attempts to explore the extent to which CATA software represented by the three variables of Frequency Distribution Analysis (FDA), Content Analysis (CA), and Key Word in Context (KWIC) incorporate with CDA decipher the manipulative purposes beyond positive presentation of selfness and negative presentation of otherness in the selected corpus. The analysis covers some CDA strategies, including justification, false statistics, and competency, for positive self-presentation; and accusation, criticism, and the use of ambiguous words for negative other-presentation. With the application of CATA, some words will be analyzed by showing their frequency distribution analysis as well as their contextual environment in the selected text to expose the extent to which they are employed as strategies of positive/negative presentation in the text under investigation. Findings show that CATA software contributes significantly to the linguistic analysis of large data texts. The paper recommends the use and application of the different CATA software in the stylistic and corpus linguistics studies.