What is lexical access?4 answersLexical access refers to the retrieval of words from the mental lexicon, which is the internal dictionary of word representations in the brain. It involves accessing the appropriate word based on its meaning and retrieving its phonological form for production. The mental lexicon is organized spatially and accessed in a temporal manner. There have been debates on the nature of lexical access, including the serial versus interactive models and the modular versus distributed organization. Recent models propose parallel and predictive models of lexical access. The speed and efficiency with which we produce words suggest that our brain is highly efficient in organizing the representations and dynamics involved in language production.
What are the different types of lexical relations?5 answersThe different types of lexical relations include homonymy, polysemy, synonymy, antonymy, hyponymy, and meronymy. Homonymy refers to words that have the same spelling or pronunciation but different meanings. Polysemy occurs when a word has multiple related meanings. Synonymy refers to words that have similar meanings. Antonymy involves words that have opposite meanings. Hyponymy is a hierarchical relationship where one word is a specific example of another word. Meronymy refers to the relationship between a whole and its parts. These types of lexical relations have been identified in various contexts, such as in songs by Kelly Clarkson, in word association tasks with Spanish-speaking older adults, in business columns in the Jakarta Post, as cohesion patterns in discourse, and in the establishment of semantic and contextual links in discourse.
What are the research done using lexicon-based approach?5 answersLexicon-based approaches have been used in several research studies. Srivastava et al. proposed a Sense Enriched Lexicon-based Approach (SELSA) that integrates Word Sense Disambiguation (WSD) into sentiment polarity classification. They compared SELSA with existing Lexicon-based approaches and supervised approaches, achieving better accuracy than the SentiWordNet-based approach without WSD. Polignano et al. developed BERT-WMAL, a hybrid model that combines transformer deep learning with a polarized lexicon for sentence polarity classification. Their model showed comparable performance to state-of-the-art approaches and provided explanations for predictions. Tan et al. implemented a lexicon-based strategy using VADER for sentiment analysis of student feedback in a Learning Management System (LMS). They aimed to create a structured representation of student sentiments to help improve the design of learning materials. Additionally, a study on predicting political security threats proposed a hybrid Lexicon-based approach combined with machine learning techniques. The Decision Tree classifier achieved the highest performance for predicting political security threats.
How can lexicon-based approaches be used to improve stock market predictions?5 answersLexicon-based approaches can be used to improve stock market predictions by incorporating sentiment analysis and generating industry-specific lexicons. These lexicons are created by analyzing raw data from social media platforms and news documents, capturing the correlation between words used in these documents and stock price fluctuations. By using lexicons, additional attributes such as sentiment polarity, valence rating, and reactivity can be obtained, which enhance the forecasting frameworks. These lexicons are then used as features in machine learning algorithms such as Decision Trees, DBSCAN, KMEANS, LSTM, and CNN to predict stock price variations. The performance evaluation of these approaches shows that they outperform other baselines and provide explainable results. Overall, lexicon-based approaches offer a valuable tool for predicting stock market trends by leveraging sentiment analysis and industry-specific lexicons.
How do different language varieties vary in their vocabulary and lexicon?5 answersDifferent language varieties vary in their vocabulary and lexicon due to a variety of factors such as historical development, language change, external influences, and social and regional identities. Regional varieties, such as British and American English, differ in sound features, vocabulary, and syntactic properties, shaped by historical events and colonization. Language change and development begin with linguistic variation, which emerges under specific historical, political, and social conditions. The diversity of languages across the world can be explained by language contact and the impact of non-native speakers on information encoding strategies. Varieties of English exhibit variation in phonology, morphology, syntax, and vocabulary, with lexical variation representing a relatively simple case. Dialects, which differ grammatically, phonologically, and lexically, are associated with specific geographical areas and social classes.
What industries has lexicon-based sentiment analysis approach?1 answersLexicon-based sentiment analysis approach has been applied in various industries. Ramesh Chundi proposed the Lexicon-based approach (NBLex) for sentiment analysis in Kannada-English code-switch text. Manuel Ojeda-Hernández et al. used Formal Concept Analysis (FCA) to create customised dictionaries for sentiment analysis in tweets. Maria Chiara Martinis et al. developed VADER-IT, a lexicon-based algorithm for polarity prediction in Italian healthcare-related reviews. Anima Srivastava et al. integrated Word Sense Disambiguation (WSD) into a Lexicon-based approach for sentiment polarity classification in movie reviews. Sergiu-George Limboi and Laura Diosan designed a lexicon-based feature for detecting the polarity of tweets. Therefore, lexicon-based sentiment analysis has been applied in fields such as code-switch text, tweets, healthcare reviews, and movie reviews.