Relevant bibliographies by topics / Lexicon based classifier

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Academic literature on the topic 'Lexicon based classifier'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Lexicon based classifier"

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Yang, Ai Min, Jiang Hao Lin, Yong Mei Zhou, and Jin Chen. "Research on Building a Chinese Sentiment Lexicon Based on SO-PMI." Applied Mechanics and Materials 263-266 (December 2012): 1688–93. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1688.

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Considering user behavior, this paper has built a Chinese sentiment lexicon based on improved SO-PMI algorithm. Sematic lexicons were used to classify the sentiment of the collected Chinese hotel reviews. The experiment has compared the feature extraction between CHI and sentiment lexicons to find out different classification performances. The results indicate that feature extraction based on sentiment lexicon gains higher F1. The performance of classification method “Basic Semantic Lexicon + BOOL + NB” gains 92.40% of F1. Based on different sentiment lexicons, the experimental results shows that (SO-A) and (SO-P) is slightly better than NB classifier. Therefore, it would be effective to use ((SO-A) and (SO-P) as text sentiment classifiers. The experiment also finds out the method “Hotel Reviews Semantic Lexicon using improved SO-PMI algorithm +((SO-A)” gains the highest F1 which is 92.84%. The results reveal that improved SO-PMI does more effective on weight calculation and sentiment lexicon building.
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Srivastava, Anima, Amit Srivastava, and Tanveer J. Siddiqui. "Sentiment Classification Using a Sense Enriched Lexicon-based Approach." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5 (2023): 208–15. http://dx.doi.org/10.17762/ijritcc.v11i5.6607.

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The prominent approach in sentiment polarity classification is the Lexicon-based approach which relies on a dictionary to assign a score to subjective words. Most of the existing work use score of the most dominant sense in this process instead of using the contextually appropriate sense. The use of Word Sense Disambiguation (WSD) is less investigated in the sentiment classification tasks. This paper investigates the effect of integrating WSD into a Lexicon-based approach for Sentiment Polarity classification and compares it with the existing Lexicon-based approaches and the state-of-art supervised approaches. The lexicon used in this work is SentiWordNet v2.0. The proposed approach, called Sense Enriched Lexicon-based Approach (SELSA), uses a word sense disambiguation module to identify the correct sense of subjective words. Instead of using the score of the most frequent sense, it uses the score of the contextually appropriate sense only. For the purpose of comparison with the supervised approaches, the authors investigate Naïve Bayes (NB) and Support Vector Machines (SVM) classifiers which tend to perform better in earlier research. The performance of these classifiers is evaluated using Word2vec, Hashing Vectorizer, and bi-gram feature. The best-performing classifier-feature combination is used for comparison. All the evaluations are done on the Movie Review dataset. SELSA achieves an accuracy of 96.25% which is significantly better than the accuracy obtained by SentiWordNet-based approach without WSD on the same dataset. The performance of the proposed algorithm is also compared with the best-performing supervised classifier investigated in this work and earlier reported works on the same dataset. The results reveal that the SVM classifier performs better than SentiWordNet approach without WSD. However, after incorporating WSD the performance of the proposed Lexicon-based approach is significantly improved and it surpasses the best-performing supervised classifier (SVM with bi-gram features).
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Mohamed, Ensaf Hussein, Mohammed ElSaid Moussa, and Mohamed Hassan Haggag. "An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding." International Journal of Computational Intelligence and Applications 19, no. 04 (2020): 2050031. http://dx.doi.org/10.1142/s1469026820500315.

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Sentiment analysis (SA) is a technique that lets people in different fields such as business, economy, research, government, and politics to know about people’s opinions, which greatly affects the process of decision-making. SA techniques are classified into: lexicon-based techniques, machine learning techniques, and a hybrid between both approaches. Each approach has its limitations and drawbacks, the machine learning approach depends on manual feature extraction, lexicon-based approach relies on sentiment lexicons that are usually unscalable, unreliable, and manually annotated by human experts. Nowadays, word-embedding techniques have been commonly used in SA classification. Currently, Word2Vec and GloVe are some of the most accurate and usable word embedding techniques, which can transform words into meaningful semantic vectors. However, these techniques ignore sentiment information of texts and require a huge corpus of texts for training and generating accurate vectors, which are used as inputs of deep learning models. In this paper, we propose an enhanced ensemble classifier framework. Our framework is based on our previously published lexicon-based method, bag-of-words, and pre-trained word embedding, first the sentence is preprocessed by removing stop-words, POS tagging, stemming and lemmatization, shortening exaggerated word. Second, the processed sentence is passed to three modules, our previous lexicon-based method (Sum Votes), bag-of-words module and semantic module (Word2Vec and Glove) and produced feature vectors. Finally, the previous features vectors are fed into 11 different classifiers. The proposed framework is tested and evaluated over four datasets with five different lexicons, the experiment results show that our proposed model outperforms the previous lexicon based and the machine learning methods individually.
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Lee, Ju-Hyoung, Sang-Ki Ko, and Yo-Sub Han. "SALNet: Semi-supervised Few-Shot Text Classification with Attention-based Lexicon Construction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 13189–97. http://dx.doi.org/10.1609/aaai.v35i14.17558.

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We propose a semi-supervised bootstrap learning framework for few-shot text classification. From a small amount of the initial dataset, our framework obtains a larger set of reliable training data by using the attention weights from an LSTM-based trained classifier. We first train an LSTM-based text classifier from a given labeled dataset using the attention mechanism. Then, we collect a set of words for each class called a lexicon, which is supposed to be a representative set of words for each class based on the attention weights calculated for the classification task. We bootstrap the classifier using the new data that are labeled by the combination of the classifier and the constructed lexicons to improve the prediction accuracy. As a result, our approach outperforms the previous state-of-the-art methods including semi-supervised learning algorithms and pretraining algorithms for few-shot text classification task on four publicly available benchmark datasets. Moreover, we empirically confirm that the constructed lexicons are reliable enough and substantially improve the performance of the original classifier.
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Rani, Sangeeta, Nasib Singh Gill, and Preeti Gulia. "Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1041–51. https://doi.org/10.11591/ijeecs.v22.i2.pp1041-1051.

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Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.
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Al Khadafi, Madonna, Kurnia Paranitha Kartika, and Filda Febrinita. "PENERAPAN METODE NAÏVE BAYES CLASSIFIER DAN LEXICON BASED UNTUK ANALISIS SENTIMEN CYBERBULLYING PADA BPJS." JATI (Jurnal Mahasiswa Teknik Informatika) 6, no. 2 (2022): 725–33. http://dx.doi.org/10.36040/jati.v6i2.5633.

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Presiden Joko Widodo menerbitkan Instruksi Presiden Republik Indonesia Nomor 1 Tahun 2022 tentang Optimalisasi Pelaksanaan Program Jaminan Kesehatan Nasional. Inpres tersebut mengatur syarat mengurus sejumlah layanan publik seperti jual beli tanah, membuat SIM, SKCK, haji dan umrah harus terdaftar sebagai peserta BPJS Kesehatan. Peraturan tersebut dimulai per 1 Maret 2022. Tetapi, yang menjadi persoalan adalah ketika berpendapat tidak berlandaskan etika, sehingga muncul adanya cemooh atau menyudutkan pihak yang bersangkutan, maka bisa mengakibatkan adanya tindak cyberbullying. Untuk itu, perlu dilakukan menganalisis sentimen pada komentar Twitter untuk mengklasifikasikan tweet yang mengandung cyberbullying atau non cyberbullying. Metode yang digunakan dalam penelitian ini adalah deskriptif kualitatif dengan algoritma pengolahan data cyberbullying menggunakan Naive Bayes Classifier dan Lexicon Based. Proses dimulai dari pengambilan data tweet, pre-processing, pembobotan TF-IDF, performa Naive Bayes Classifier. Kemudian dilakukan proses klasifikasi menggunakan metode Lexicon Based dengan hasil keluaran sistem berupa identifkasi apakah tweet termasuk cyberbullying atau non cyberbullying. Pada penelitian ini, didapatkan hasil performansi dari Naive Bayes Classifier menghasilkan akurasi 80%, sedangkan Lexicon Based menghasilkan akurasi 22%. Setelah membandingkan kedua metode, maka kesimpulan yang dapat diambil adalah Naive Bayes Classifier lebih baik dan lebih akurat daripada Lexicon Based.
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Setiawan, Samuel Budi, and Auliya Rahman Isnain. "Sentimen Analisis Masyarakat Terhadap Pembangunan IKN Menggunakan Algoritma Lexicon Based Approach dan Naïve Bayes." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 2 (2024): 1019. http://dx.doi.org/10.30865/mib.v8i2.7506.

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The relocation and construction of IKN (Capital City of the Archipelago) as a center for state administration activities has many benefits and shortcomings, starting from the selection of locations, the ratification of laws that are considered too hasty then raises pros and cons by the Indonesian people. President Joko Widodo decided to move the country's capital outside Java in a meeting on April 29, 2019. The location of the IKN development was determined in East Kalimantan. This research was conducted by retrieving data via Twitter with the keyword "IKN Development". The data that has been collected totals 3,680 tweets. Data analysis was carried out with two methods, namely Naïve Bayes Classifier and Lexicon Based, and the best accuracy value was found between the two methods in analyzing data on public responses to IKN Development. The initial step of the data analysis process is the preprocessing process which contains stages such as labelling, case folding, cleaning, tokenizing, stopword removal, stemming. It is known that the results obtained from the analysis of the Naïve Bayes Classifier method have an accuracy value of 79%, and Lexicon Based has an accuracy value of 76%. Sentiment analysis of the two methods has Positive, Negative, and Neutral sentiments. With the stages of the analysis process using the Naïve Bayes Classifier and lexicon based methods, it can be seen that the Naïve Bayes Classifier method shows a Positive sentiment of 47.18%, Negative of 6.33%, and Neutral of 46.49%, while for Lexicon Based, Positive sentiment reaches 54.15%, Negative 29.36%, and Neutral 16.49%. It should be noted that the highest positive polarity result is found in the Lexicon Based algorithm at 54.15%, while in the Naïve Bayes Classifier 47.18%. It can be concluded from the results of both methods that Naïve Bayes Classifier has a better analysis compared to Lexicon-Based analysis.
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Mohd, Zeeshan Ansari, Ahmad Tanvir, Mohd Sufyan Beg Mirza, and Bari Noaima. "Language lexicons for Hindi-English multilingual text processing." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 641–48. https://doi.org/10.11591/ijai.v11.i2.pp641-648.

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Language identification (LI) in textual documents is the process of automatically detecting the language contained in a document based on its content. The present language identification techniques presume that a document contains text in one of the fixed set of languages. However, this presumption is incorrect when dealing with multilingual document which includes content in more than one possible language. Due to the unavailability of standard corpora for Hindi-English mixed lingual language processing tasks, we propose the language lexicons, a novel kind of lexical database that augments several bilingual language processing tasks. These lexicons are built by learning classifiers over English and transliterated Hindi vocabulary. The designed lexicons possess condensed quantitative characteristics which reflect their linguistic strength in respect of Hindi and English language. On evaluating the lexicons, it is observed that words of the same language tend to cluster together and are separable over language classes. On comparing the classifier performance with existing works, the proposed lexicon models exhibit the better performance.
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Faizal, Ahmad, Agung Susilo Yuda Irawan, and Didi Juardi. "Perbandingan Lexicon Based Dan Naïve Bayes Classifier Pada Analisis Sentimen Pengguna Twitter Terhadap Gempa Turki." INTECOMS: Journal of Information Technology and Computer Science 6, no. 2 (2023): 1037–48. http://dx.doi.org/10.31539/intecoms.v6i2.7360.

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Peristiwa bencana Gempa Turki yang menelan banyak korban jiwa sedang ramai saat ini baik di media nasional maupun media internasional, hal ini menyebabkan munculnya banyak opini pengguna sosial media teruma dalam Platform Twitter. Tweet yang diposting oleh pengguna sosial media Twitter tersebut kemudian dapat dijadikan sumber informasi yang bermanfaat. Dikarenakan hal tersebut, analisis sentimen dapat digunakan sebagai solusi untuk mengolah suara tersebut dengan menggunakan pendekatan Lexicon Based dan Naïve Bayes Classifier. Tujuan dari penelitian ini adalah untuk mengklasifikasikan pendapat tentang peristiwa Bencana Gempa yang terjadi di Turki pada 6 Februari 2023 berdasarkan kelas sentimen positif, sentimen netral dan sentimen negatif. Skenario 90:10 digunakan untuk pengujian. Hasil evaluasi menunjukkan bahwa pengujian pendekatan Lexicon Based dan Naïve Bayes Classifier menghasilkan nilai akurasi sebesar 65%. Sedangkan Naïve Bayes Classifier tanpa pendekatan Lexicon Based menghasilkan nilai akurasi sebesar 64%.
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Nang, Noon Kham. "Lexicon Based Emotion Analysis on Twitter Data." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1008–12. https://doi.org/10.5281/zenodo.3590493.

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This paper presents a system that extracts information from automatically annotated tweets using well known existing opinion lexicons and supervised machine learning approach. In this paper, the sentiment features are primarily extracted from novel high coverage tweet specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment word hashtags and from tweets with emoticons. The sentence level or tweet level classification is done based on these word level sentiment features by using Sequential Minimal Optimization SMO classifier. SemEval 2013 Twitter sentiment dataset is applied in this work. The ablation experiments show that this system gains in F Score of up to 6.8 absolute percentage points. Nang Noon Kham "Lexicon Based Emotion Analysis on Twitter Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26566.pdf
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Book chapters on the topic "Lexicon based classifier"

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Das, Dipankar, Soujanya Poria, and Sivaji Bandyopadhyay. "A Classifier Based Approach to Emotion Lexicon Construction." In Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31178-9_41.

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Hernández-López, Juanita, and Wilfrido Gómez-Flores. "A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography." In XXVII Brazilian Congress on Biomedical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70601-2_240.

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Nguyen-Thi, Bich-Tuyen, and Huu-Thanh Duong. "A Vietnamese Sentiment Analysis System Based on Multiple Classifiers with Enhancing Lexicon Features." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30149-1_20.

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Mery, Bruno, and Christian Retoré. "Classifiers, Sorts, and Base Types in the Montagovian Generative Lexicon and Related Type Theoretical Frameworks for Lexical Compositional Semantics." In Studies in Linguistics and Philosophy. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50422-3_7.

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Puspita, Rizma A., and Bernadette Kushartanti. "Lexical Cohesion in the Narrative Produced by Javanese School-Age Children in Pati." In Language Practices Among Children and Youth in Indonesia. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4775-1_4.

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AbstractThis chapter presents a study which discussed the narrations in Bahasa Indonesia by children in Pati, Central Java, who still use Javanese at home. Our focus is on lexical cohesive devices, based on Halliday and Hasan’s (Cohesion in English. Longman, 1976) theory. The participants in this study are school-age children (N = 51) aged 6–9 years old, classified into three groups based on their grades at school. This study uses semi-structured elicitation technique with silent film The Pear Story (developed by Chafe in The pear film, 1975) as the main instrument. Instruction is in Indonesian. The analysis is supported by our observation at school and parental questionnaires on language use. Descriptive statistics analysis is conducted to observe the tendency of the use of the lexical cohesive device. It is found that children use Indonesian- and Javanese cohesive devices in their story and use code-mixing, as well. The result shows that the use of repetition cohesive device in Indonesian is being very dominant, compared to other cohesive devices. The most dominant ones in all grades are the repetitions of nouns and verbs.
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Bose, Saugata, and Guoxin Su. "LexiFusedNet: A Unified Approach for Imbalanced Short-Text Classification Using Lexicon-Based Feature Extraction, Transfer Learning and One Class Classifiers." In Knowledge Management and Acquisition for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7855-7_6.

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Bravo-Marquez Felipe, Frank Eibe, and Pfahringer Bernhard. "Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-672-9-498.

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The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. A drawback of these approaches is the high cost involved in data annotation. Two freely available resources that can be exploited to solve the problem are: 1) large amounts of unlabelled tweets obtained from the Twitter API and 2) prior lexical knowledge in the form of opinion lexicons. In this paper, we propose Annotate-Sample-Average (ASA), a distant supervision method that uses these two resources to generate synthetic training data for Twitter polarity classification. Positive and negative training instances are generated by sampling and averaging unlabelled tweets containing words with the corresponding polarity. Polarity of words is determined from a given polarity lexicon. Our experimental results show that the training data generated by ASA (after tuning its parameters) produces a classifier that performs significantly better than a classifier trained from tweets annotated with emoticons and a classifier trained, without any sampling and averaging, from tweets annotated according to the polarity of their words.
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Al-Sheikh, Eman S., and Mozaherul Hoque Abul Hasanat. "Social Media Mining for Assessing Brand Popularity." In Global Branding. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9282-2.ch039.

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Businesses seek to analyse their customer feedback to compare their brand's popularity with the popularity of competing brands. The increasing use of social media in recent years is producing large amounts of textual content, which has become rich source of data for brand popularity analysis. In this article, a novel hybrid approach of classification and lexicon based methods is proposed to assess brand popularity based on the sentiments expressed in social media posts. Two different classification models using Naïve Bayes (NB) and SVM are built based on Twitter messages for 9 different brands of 3 cosmetic products. In addition, sentiment quantification have been performed using a lexicon-based approach. Based on the overall comparison of the proposed models, the SVM classifier has the highest performance with 78.85% accuracy and 94.60% AUC, compared to 73.57% and 63.63% accuracy, 80.63% and 69.38% AUC of the NB classifier and the sentiment quantification approach respectively. Specific indices based on classification and lexicon approaches are proposed to assess the brand popularity.
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Broadwell, Peter, Nicole Davis, and Sunmoo Yoon. "Using Artificial Intelligence to Develop a Lexicon-Based African American Tweet Detection Algorithm to Inform Culturally Sensitive Twitter-Based Social Support Interventions for African American Dementia Caregivers." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti210844.

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We extracted 3,291,101 Tweets using hashtags associated with African American-related discourse (#BlackTwitter, #BlackLivesMatter, #StayWoke) and 1,382,441 Tweets from a control set (general or no hashtags) from September 1, 2019 to December 31, 2019 using the Twitter API. We also extracted a literary historical corpus of 14,692 poems and prose writings by African American authors and 66,083 items authored by others as a control, including poems, plays, short stories, novels and essays, using a cloud-based machine learning platform (Amazon SageMaker) via ProQuest TDM Studio. Lastly, we combined statistics from log likelihood and Fisher’s exact tests as well as feature analysis of a batch-trained Naive Bayes classifier to select lexicons of terms most strongly associated with the target or control texts. The resulting Tweet-derived African American lexicon contains 1,734 unigrams, while the control contains 2,266 unigrams. This initial version of a lexicon-based African American Tweet detection algorithm developed using Tweet texts will be useful to inform culturally sensitive Twitter-based social support interventions for African American dementia caregivers.
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Ananth kumar, T., Zaafira J., P. Kanimozhi, Rajmohan R., Christo Ananth, and Sunday Adeola Ajagbe. "Machine Learning and Sentiment Analysis." In AI-Driven Marketing Research and Data Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2165-2.ch014.

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Customer feedback shapes businesses and improves customer experiences in the age of advanced technology and interconnectedness. This study uses machine learning in sentiment analysis to gain customer feedback insights. An efficient and automated method to analyze large volumes of customer comments, reviews, and opinions will help businesses make data-driven decisions. The study begins with sentiment analysis, machine learning, and natural language processing theory. Lexicon-based, machine learning classifier, and deep learning sentiment analysis methods are compared for customer feedback data handling. Next, a large dataset of customer feedback samples from online sources, social media, and review sites is collected. Preprocessing the data handles noise, missing values, and feature extraction to make it suitable for machine learning algorithms. The experimental phase uses several cutting-edge machine learning models to analyze customer feedback sentiment. The proposed work also examines ensemble and transfer learning methods to improve model performance.
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Conference papers on the topic "Lexicon based classifier"

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K R, Sushmitha, Rangalakshmi G R, and Suguna A. "Sentiment Analysis of Incoming Voice Calls." In International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/bisl3666/icrcct24p19.

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This project aims to meet the increasing need for real time sentiment analysis within voice call interactions, acknowledging the rising significance of voice based engagements in today’s telecommunications realm. For instance, pre trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N grams, BERT, and CNN. In the model, sentiment lexicon, N grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets
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Adikara, Putra Pandu, Sigit Adinugroho, and Salsabila Insani. "Detection of cyber harassment (cyberbullying) on Instagram using naïve bayes classifier with bag of words and lexicon based features." In SIET '20: 5th International Conference on Sustainable Information Engineering and Technology. ACM, 2020. http://dx.doi.org/10.1145/3427423.3427436.

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Aleksandrova, Desislava, and Vincent Pouliot. "CEFR-based Contextual Lexical Complexity Classifier in English and French." In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.bea-1.43.

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Kramer, Jared, and Clara Gordon. "Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features." In Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014). Association for Computational Linguistics and Dublin City University, 2014. http://dx.doi.org/10.3115/v1/s14-1003.

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Roemmele, Melissa, Sosuke Kobayashi, Naoya Inoue, and Andrew Gordon. "An RNN-based Binary Classifier for the Story Cloze Test." In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-0911.

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A, Thahira, and Ansamma John. "Phishing Website Detection Using LGBM Classifier With URL-Based Lexical Features." In 2022 IEEE Silchar Subsection Conference (SILCON). IEEE, 2022. http://dx.doi.org/10.1109/silcon55242.2022.10028793.

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SECRIERU, Cristina, and Natalia STRATAN. "Classification criteria of medical terms in Hortensia Papadat-Bengescu's works (based on the novels Fecioarele despletite and Bach Concert)." In "Educaţia lingvistică şi literară în contextul dezvoltării valorilor general-umane", conferinţă ştiinţifică internaţională. Ion Creangă Pedagogical State University, 2024. https://doi.org/10.46727/c.10-11-11-2023.p49-55.

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The present research had in mind the criteria for classifying medical terms in the nov Fecioarele despletite and Bach Music Concert by Hortensia Papadat-Bengescu. Thus, the medical terms were classified: according to the formal criterion, according to the morphological criterion, according to the etymological criterion, according to the lexical-semantic criterion.
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GUAN, Xiaowei, and Qingshi GAO. "CHINESE FORMALIZED CLASSIFIER SELECTION BASED ON LEXICAL EXAMPLES AND SEMANTIC COLLOCATION IN MT." In 11th Joint International Computer Conference - JICC 2005. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701534_0100.

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de Souza, Aleksander Tomaz, and Evandro Eduardo Seron Ruiz. "Lexical noun phrase chunking with Universal Dependencies for Portuguese." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/stil.2023.25482.

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Partial parsing retrieves a limited amount of syntactic information from a sentence. This project describes the identification of a specific type of noun phrase, through partial syntactic analysis, defined as a lexical noun phrase (NPL), in texts written in Brazilian Portuguese, and annotated according to the Universal Dependency (UD) formalism. The Transformation Based Learning algorithm, TBL–Brill, applied as baseline, obtained an accuracy of 87.42% considering the UD dependency relations and 91.44% considering the UD morphosyntactic tags. Two other classifiers, one based on binary trees and the other based on a decision forest, had inferior performance.
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10

Katargulov, Dmitrii N. "MEANS OF EXPRESSIVENESS IN AMERICAN POLITICAL DISCOURSE (BASED ON DONALD J. TRUMP’S SPEECHES AT THE TV DEBATES OF 2020)." In II All-Russian scientific-practical conference with international participation "Translation and foreign languages in the global dialogue of cultures". St. Petersburg State University, 2024. http://dx.doi.org/10.21638/11701/9785288064289.11.

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This article examines the means of expressiveness used by the 45th President of the United States in the television election debates of 2020. We studied transcripts of two TV debates, analyzed the functions of the expressive means used and classified them according to linguistic levels. Based on the results of the study, we identified the most frequent means of expressiveness, such as metaphors, idioms, euphemisms and lexical repetition.
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