Classification Of Words Research Articles

Disinformation, Fakes and Propaganda Identifying Methods in Online Messages Based on NLP and Machine Learning Methods

A new method of propaganda analysis is proposed to identify signs and change the dynamics of the behaviour of coordinated groups based on machine learning at the processing disinformation stages. In the course of the work, two models were implemented to recognise propaganda in textual data - at the message level and the phrase level. Within the framework of solving the problem of analysis and recognition of text data, in particular, fake news on the Internet, an important component of NLP technology (natural language processing) is the classification of words in text data. In this context, classification is the assignment or assignment of textual data to one or more predefined categories or classes. For this purpose, the task of binary text classification was solved. Both models are built based on logistic regression, and in the process of data preparation and feature extraction, such methods as vectorisation using TF-IDF vectorisation (Term Frequency – Inverse Document Frequency), the BOW model (Bag-of-Words), POS marking (Part-Of-Speech), word embedding using the Word2Vec two-layer neural network, as well as manual feature extraction methods aimed at identifying specific methods of political propaganda in texts are used. The analogues of the project under development are analysed the subject area (the propaganda used in the media and the basis of its production methods) is studied. The software implementation is carried out in Python, using the seaborn, matplotlib, genism, spacy, NLTK (Natural Language Toolkit), NumPy, pandas, scikit-learn libraries. The model's score for propaganda recognition at the phrase level was obtained: 0.74, and at the message level: 0.99. The implementation of the results will significantly reduce the time required to make the most appropriate decision on the implementation of counter-disinformation measures concerning the identified coordinated groups of disinformation generation, fake news and propaganda. Different classification algorithms for detecting fake news and non-fakes or fakes identification accuracy from Internet resources ana social mass media are used as the decision tree (for non-fakes identification accuracy 0.98 and fakes identification accuracy 0.9903), the k-nearest neighbours (0.83/0.999), the random forest (0.991/0.933), the multilayer perceptron (0.9979/0.9945), the logistic regression (0.9965/0.9988), and the Bayes classifier (0.998/0.913). The logistic regression (0.9965) the multilayer perceptron (0.9979) and the Bayesian classifier (0.998) are more optimal for non-fakes news identification. The logistic regression (0.9988), the multilayer perceptron (0.9945), and k-nearest neighbours (0.999) are more optimal for identifying fake news identification.

On generalized concise words

Abstract The study of verbal subgroups within a group is well known for being an effective tool to obtain structural information about a group. Therefore, conditions that allow the classification of words in a free group are of paramount importance. One of the most studied problems is to establish which words are concise, where a word 𝑤 is said to be concise if the verbal subgroup w ⁢ ( G ) w(G) is finite in each group 𝐺 in which 𝑤 takes only a finite number of values. The purpose of this article is to present some results, in which a hierarchy among words is introduced, generalizing the concept of concise word.

Swear Words Used by Students in Daily Communication

This research focuses on 1) the classification of swearing sources used by students of STBA JIA and 2) the classification of the use of swearing proposed by students of STBA JIA. The theory used in this research is Wardhaugh's theory (1986:230); Hughes (1991:208); Rothwell (1973:108); Trudgill (1983) to classify sources and reasons or functions of swearing. This research is qualitative with descriptive methods. The data taken came from questionnaire answers by respondents consisting of 14 male students and 43 female students. This research found that there are 12 sources of swearing used by male respondents and 12 sources of swearing used by female respondents. There are a total of 12 sources of invective, consisting of 7 sources based on experts and 5 new sources. Then, regarding the reasons for using swear words, male respondents stated 16 reasons and female respondents stated 18 reasons. The total number of reasons for using swear words in this study was 24. Furthermore, related to research findings based on the classification of swear words, there is no difference in the classification of sources and the reasons for using swearing words between the reasons for male respondents and the reasons for female respondents

Устаревшая лексика в карачаево-балкарском нартском эпосе

This article considers the lexical layer of the language, the study of which contributes to a more in-depth understanding of the culture, life, traditions of the people. Outdated vocabulary, repre-sented by historicisms and archaisms, is one of the under-explored layers of vocabulary and is the most mobile level of language in which various changes constantly occur. The reasons for archa-ization can be different, i.e. they can be associated with social transformations in the life of socie-ty, and can also be because of linguistic laws. The article attempted to lexical semantic classifica-tion of outdated words found in the texts of the Karachay-Balkarian heroic epic “Narts.” The re-sults obtained showed that outdated words of the Karachay-Balkarian language can be combined into the following thematic groups depending on their belonging to certain spheres of life: mili-tary terminology, class position, human activity, objects and phenomena of nature, names of weight measure, distance, etc. It should be noted that in the texts of the Nart epic, in addition to outdated substantive vocabulary, there are adjectives and verbs. It should also be noted that, in ep-ic texts, lexical archaisms prevail over semantic ones.

Word embedding and classification methods and their effects on fake news detection

Natural language processing contains multiple methods of translating written text or spoken words into numerical information called word embeddings. Some of these embedding methods, such as Bag of Words, assume words are independent of one another. Other embedding methods, such as Bidirectional Encoder Representations from Transformers and Word2Vec, capture the relationship between words in various ways. In this paper, we are interested in comparing methods treating words as independent and methods capturing the relationship between words by looking at the effect these methods have on the classification of fake news. Using various classification methods, we compare the word embedding processes based on their effects on accuracy, precision, sensitivity, and specificity.

MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge

Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations.

Challenges and prospects for teaching ocean literacy in Brazilian schools

This study took as a case study the perspective of teachers working in the State of Rio de Janeiro by 53 interviews to investigate the challenges and prospects of including content about the ocean in Brazilian school classrooms. The data were subjected to content analysis and explored quantitatively using Descending Hierarchical Classification (DHC) and Factor Correspondence Analysis (FCA) of word and terms used by teachers in their discourse. The main difficulties reported in including themes related to the ocean and marine environments in classes were the absence of OL in the school curriculum and university education, as well as the limited time to work on these subjects in classes, which is in line with the challenges faced by teachers from other countries. On the other hand, Brazilian teachers cited particular problems of Brazilian formal education, such as the need for professional development, support for an education with an interdisciplinary emphasis, and provision of financial resources for schools. However, DHC analysis also indicates strong connection of the teachers with the ocean due to sea fascination (24% of the answers in the textual corpus), spiritual and emotional connection (26.7%), leisure (22.7%) and professional interest (26.7%). Furthermore, the textual corpus also identified that teachers recognize the importance of teaching marine topics due to: students live in coastal areas (32.2%), the provision of ecosystem services (19.9%), preservation issues (34.2%), and limited student knowledge on these themes (13.7%). These data can help formulate educational policies to mitigate OL teaching barriers in Brazilian schools.

LipNet: End-to-End Lipreading

Lipreading is the task of decoding text from the movement of a speaker’s mouth. This research presents the development of an advanced end-to-end lipreading system. Leveraging deep learning architectures and multimodal fusion techniques, the proposed system interprets spoken language solely from visual cues, such as lip movements. Through meticulous data collection, annotation, preprocessing, model development, and evaluation, diverse datasets encompassing various speakers, accents, languages, and environmental conditions are curated to ensure robustness and generalization. Conventional methods divided the task into two phases: prediction and designing or learning visual characteristics. Most deep lipreading methods are trainable from end to end. In the past, lipreading has been tackled using tedious and sometimes unsatisfactory techniques that break down speech into smaller units like phonemes or visemes. But these methods often fail when faced with real-world problems, such contextual factors, accents, and differences in speech patterns. Nevertheless, current research on end-to-end trained models only carries out word classification; sentence-level sequence prediction is not included. LipNet is an end-to-end trained model that uses spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss to translate a variable-length sequence of video frames to text. LipNet breaks from this traditional paradigm by using an all-encompassing, end-to-end approach supported by deep learning algorithms, Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are skilled at processing sequential data and extracting high-level representations, are fundamental to LipNet's architecture.LipNet achieves 95.2% accuracy in sentence-level on the GRID corpus, overlapped speaker split task, outperforming experienced human lipreaders and the previous 86.4% word-level state-of-the-art accuracy.The results underscore the transformative potential of the lipreading system in real-world applications, particularly in domains such as assistive technology and human-computer interaction, where it can significantly improve communication accessibility and inclusivity for individuals with hearing impairments.

Intelligent event-based lip reading word classification with spiking neural networks using spatio-temporal attention features and triplet loss

Lip reading is a visual alternative to enhance the intelligence of traditional speech recognition, which can benefit from retina-like event cameras that focus on dynamic movements. Spiking Neural Networks (SNNs) are inherently well-suited to cooperate with event cameras. However, employing SNN for event-based lip reading presents significant challenges, particularly in effectively extracting spatio-temporal features from input events and distinguishing between phonetically similar elements. To address these challenges, this paper proposes a novel event-based lip-reading model that leverages the SNN framework, enriched by a designed Spatial-Temporal Attention Block (STAB) and a triplet loss. Specifically, STAB comprises both spatial and temporal attention branches, dynamically emphasizing those spatial and temporal characteristics most reflective of lip movements. STAB also includes a fusion mechanism to integrate these spatial and temporal insights, providing a comprehensive and focused representation of lip movements. In addition, we enhance the model by incorporating triplet loss into the SNN training process, further improving the model's ability to distinguish between visually similar words. Experimental results show the superior performance of our SNN and validate the effect of STAB and triplet loss. We also conduct the energy consumption analysis to affirm the model's energy efficiency.

Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging

Objective. Brain-computer interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent functional magnetic resonance imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis. Approach. Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass support vector machine (SVM). Main results. Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus and left planum temporale in addition to the SMC. Significance. The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.

Is predicting during language processing worth it? Effects of cloze probability and semantic similarity on failed predictions.

Prediction during language processing has been hypothesized to lead to processing benefits. These possible benefits have led to several prominent theories that center around prediction as an essential mechanism in language processing. Such theories typically assume predicting is better than not predicting at all, but do not always account for the potential processing costs from failed predictions. Predicting wrongly can be costly, but the cost may depend on how wrong the prediction was. Across three experiments, we manipulate cloze probability, semantic relatedness, and language modality (production vs. comprehension) to determine whether predicting almost correctly is better than predicting completely incorrectly, and if so, if predicting almost correctly is better than not predicting at all. Results showed that when a predicted ending is replaced with a related term, it is processed faster than when it is replaced with an unrelated term, but that related term is not named more quickly than when it appears after a low constraint sentence. This pattern held regardless of whether participants were asked to produce the sentence-final term by naming a picture (Experiments 1 and 2), or if they were asked to perform a semantic classification of the sentence-final word (Experiment 3). Thus, predicting almost correctly is better than predicting completely incorrectly, but it's not better than not predicting at all. This carries implications for current accounts that argue for processing benefits of prediction during language processing, and suggests that prediction may be used to fine-tune the language system rather than to speed language processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A sensor system integrating sensing and intelligence based on MEMS reservoir computing

Reservoir computing (RC) is a bio-inspired neural network structure which is easy to be implemented in hardware. It has been constructed in a great many fields such as memristor, electrochemical reaction, among which MEMS is the closest to integrate sensing and computing. We propose a novel sensor system of MEMS RC based on stiffness modulation, that natural signal directly affects the system stiffness as the input. Under this paradigm, information can be processed locally without data collection and pre-processing. We inherited the nonlinearity tuning principle and optimized the post-processing algorithm by creating a digital mask operator. In this way, the system can deal with classification tasks as well as forecasting tasks. We integrated MEMS, IC and FPGA with a small volume and low power consumption, so complicated setup for data discretization and transduction in traditional MEMS RC is eliminated. The system can process word classification and chaos forecasting with high accuracy, which preliminarily proves the integrated RC architecture.

КЫРГЫЗ БАЛДАР АДАБИЯТЫНДА ТУУРАНДЫ СӨЗДӨРДҮН ӨЗГӨЧӨЛҮГҮ

The article examines the phonetic features and stylistic nature ofimitative words presented in Kyrgyz children's works. In Kyrgyz children's literature, many issues of studying words as functionally significant units of language today are not considered in sufficient depth. Ourgoal is to establish linguistic units, phonetic features and stylistic ways of usingimitativewords in children's fiction of the Kyrgyz people. As children acquire language, comparisons include creativity as a recurring component. When describing a child’s linguistic world, it is important to determine the appropriate parameters: people, nature, linguistic world. Among these areas, we noted that the function of comparisons in the world of language is special, andwe examined their problem as a component of the sound system of the language and their stylistic nature using the example of the works of Kyrgyz children. The semantic classification of imitative words in children's literature is also one of the main issues. Therefore, they were divided into groups depending on their importance.

Words, Sense, and Denotation Meaning in Pupuh Ginanti “Saking Tuhu Manah Guru”

This study aims to analyze words, sense, and denotation in Pupuh Ginanti, entitled "Saking Tuhu Manah Guru". Pupuh is one of the Balinese poem that is still preserved nowadays. This pupuh has a moral value that needs to be conveyed to listeners. The message is conveyed through the form of the words that compose it. Every word or sentence in a literary work has meant that unknowingly, so an analysis of the word, sense, and meaning of denotation in Pupuh Ginanti needs to be discussed more deeply. Data was initially categorised for this study using some kind of word classification. Start data coding after categorization. By analysing text-based data, data analysis techniques employ qualitative methods. The researcher also employed documentation strategies to obtain the data. The theory used in this study is (Allan, 2014) to analyze sense meaning and supported by theory from (Kreidler, 2002) to analyze denotation meaning. This study uses a descriptive qualitative approach. The result of this research is that the meaning of the poem in Pupuh Ginanti entitled "Saking tuhu manah guru" has a denotative meaning and sense meaning in each line. Sense meaning in this poem was used to clarify the message to be conveyed by the author.

Analyzing Robustness of Angluin's L$^*$ Algorithm in Presence of Noise

Angluin's L$^*$ algorithm learns the minimal deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approximatively correct (PAC) version substitutes an equivalence query by numerous random membership queries to get a high level confidence to the answer. Thus it can be applied to any kind of device and may be viewed as an algorithm for synthesizing an automaton abstracting the behavior of the device based on observations. Here we are interested on how Angluin's PAC learning algorithm behaves for devices which are obtained from a DFA by introducing some noise. More precisely we study whether Angluin's algorithm reduces the noise and produces a DFA closer to the original one than the noisy device. We propose several ways to introduce the noise: (1) the noisy device inverts the classification of words w.r.t. the DFA with a small probability, (2) the noisy device modifies with a small probability the letters of the word before asking its classification w.r.t. the DFA, (3) the noisy device combines the classification of a word w.r.t. the DFA and its classification w.r.t. a counter automaton, and (4) the noisy DFA is obtained by a random process from two DFA such that the language of the first one is included in the second one. Then when a word is accepted (resp. rejected) by the first (resp. second) one, it is also accepted (resp. rejected) and in the remaining cases, it is accepted with probability 0.5. Our main experimental contributions consist in showing that: (1) Angluin's algorithm behaves well whenever the noisy device is produced by a random process, (2) but poorly with a structured noise, and, that (3) is able to eliminate pathological behaviours specified in a regular way. Theoretically, we show that randomness almost surely yields systems with non-recursively enumerable languages.

The time course of the spatial representation of ‘past’ and ‘future’ concepts: New evidence from the STEARC effect

Humans use space to think of and communicate the flow of time. This spatial representation of time is influenced by cultural habits so that in left-to-right reading cultures, short durations and past events are mentally positioned to the left of long durations and future events. The STEARC effect (Space Temporal Association of Response Codes) shows a faster classification of short durations/past events with responses on the left side of space and of long durations/future events with responses on the right side. We have recently showed that during the classification of time durations, space is a late heuristic of time because in this case, the STEARC appears only when manual responses are slow, not when they are fast. Here, we wished to extend this observation to the semantic classification of words as referring to the ‘past’ or the ‘future’. We hypothesised that the semantic processing of ‘past’ and ‘future’ concepts would have engaged slower decision processes than the classification of short versus long time durations. According to dual-route models of conflict tasks, if the task-dependent classification/decision process were to proceed relatively slowly, then the effects of direct activation of culturally preferred links between stimulus and response (S-R), i.e., past/left and future/right in the case of the present task, should attain higher amplitudes before the instruction-dependent correct response is selected. This would imply that, at variance with the faster classification of time durations, during the slower semantic classification of time concepts, in incongruent trials, the direct activation of culturally preferred S-R links should introduce significant reaction time (RT) costs and a corresponding STEARC at the fastest manual responses in the experiment too. The study's results confirmed this hypothesis and showed that in the classification of temporal words, the STEARC also increased as a function of the length of RTs. Taken together, the results from sensory duration and semantic classification STEARC tasks show that the occurrence, strength and time course of the STEARC varies significantly as a function of the speed and level of cognitive processing required in the task.

MORPHOLOGY AND MORPHEMOLOGY: WORD STRUCTURE AND MORPHOLOGICAL ASPECTS OF LANGUAGE

The article focuses on analyzing the foundational theoretical tenets of morphemology and morphology within contemporary Ukrainian linguistics, emphasizing their intrinsic interrelation. A comprehensive examination of the morpheme as the fundamental linguistic entity for morphemological studies is undertaken. The primary attributes of a morpheme as a linguistic unit are elucidated, facilitating a more proficient comprehension of the principles underlying the morphemic analysis of words. Furthermore, the research delves into the grammatical meanings embodied by morphemes within specific parts of speech, drawing on Ukrainian language lexemes for illustration. The discourse provides a rationale for considering morphemology as an autonomous branch of linguistics, intricately linked with morphology and derivatology. The article delineates distinctions in the application of terms such as "morphemics" versus "morphemology" and "morpheme" versus "morph." A crucial assertion is advanced, substantiating that a morpheme, functioning as the smallest semantically significant constituent of a word, can simultaneously convey lexical, grammatical, and word-formational meanings. The correlation between morphemology and morphology is substantiated by the integral semantics inherent in words, a phenomenon derived from the amalgamation of lexical and grammatical meanings. The confluence of these meanings establishes a word's classification within a specific grammatical category (part of speech) and its affiliation with a particular word-formational type. Similar to a word form, a morpheme exhibits reproducibility in language, possesses a semiological function, and serves to convey both subject (via the root) and non-subject (via affixes) meanings. As with other linguistic entities, morphemes and word forms can be regarded as historical categories, prompting the differentiation of word formation into historical and synchronic aspects. Throughout the historical evolution of a language, alterations in word structure manifest through phonetic changes, the loss of productive affixes, and modifications in the phonemic boundaries of morphemes. Consequently, the framework for synchronic morphemic analysis must not be indiscriminately applied to other synchronic facets of the language.

KATA TABU DALAM DIALOG TOKOH PADA FILM BETTER DAYS 《少年的你》

Film is one of the literary works in the form of audio-visual, serving as a medium to convey messages depicted through both sound and images, along with all its supporting elements. The term 'taboo' is one of the sociolinguistic studies that we often hear; generally, taboo words go against the prevailing norms and moral values. In this research, the author aims to explore what kind of taboo words are present in the film 'Better Days' (《少年的你》) and classify them according to their types. The goal is to identify the types of taboo words used in the dialogue between characters, using the taboo word classification according to The Jay.

An empirical assessment of different word embedding and deep learning models for bug assignment

Bug assignment, or bug triage, focuses on identifying the appropriate developers to repair newly discovered bugs, thereby managing them more effectively. Several deep learning-based approaches have been proposed for automated bug assignment. These approaches view automated bug assignment as a text classification task — the textual description of a bug report is utilized as the input and the potential fixers are regarded as the output labels. Such approaches typically depend on the classification performance of natural language processing and machine learning techniques. Various word embedding and deep learning models have emerged continuously. The effectiveness of those approaches depends on the chosen deep learning model, used for classification, and the word embedding model, used for representing bug reports. However, prior research does not empirically evaluate the impacts of various word embedding and deep learning models for automated bug assignment. In this paper, we conduct an empirical study to analyze the performance variations among 35 deep learning-based automated bug assignment approaches. These approaches are based on five word embedding techniques, i.e., Word2Vec, GloVe, NextBug, ELMo, and BERT, and seven text classification models, i.e., TextCNN, LSTM, Bi-LSTM, LSTM with attention, Bi-LSTM with attention, MLP, and Naive Bayes. We evaluated these combinations across three benchmark datasets, namely Eclipse JDT, GCC, and Firefox, and their mergence i.e., a cross-project dataset. Our main observations are: (1) Bi-LSTM with attention and Bi-LSTM using ELMo are significantly superior to other deep learning models on bug assignment tasks in terms of top-k (k = 1, 5, 10) accuracy and MRR; (2) Both the summary and description of bug reports are useful for bug assignment, but the description is more useful than the summary; (3) The training corpus for word embedding models has a significant impact on the performance of deep learning-based bug assignment methods. Our results show the importance of tuning different components (e.g. word embedding model, classification model, and textual input) in deep learning-based automated bug assignment methods and provide important insights for practitioners and researchers.

Demonstration-Based and Attention-Enhanced Grid-Tagging Network for Mention Recognition

Concepts empower cognitive intelligence. Extracting flat, nested, and discontinuous name entities and concept mentions from natural language texts is significant for downstream tasks such as concept knowledge graphs. Among the algorithms that uniformly detect these types of name entities and concepts, Li et al. proposed a novel architecture by modeling the unified mention recognition as the classification of word–word relations, named W2NER, achieved state-of-the-art (SOTA) results in 2022. However, there is still room for improvement. This paper presents three improvements based on W2NER. We enhanced the grid-tagging network by demonstration learning and tag attention feature extraction, so our modified model is named DTaE. Firstly, addressing the issue of insufficient semantic information in short texts and the lack of annotated data, and inspired by the demonstration learning from GPT-3, a demonstration is searched during the training phase according to a certain strategy to enhance the input features and improve the model’s ability for few-shot learning. Secondly, to tackle the problem of W2NER’s subpar recognition accuracy problem for discontinuous entities and concepts, a multi-head attention mechanism is employed to capture attention scores for different positions based on grid tagging. Then, the tagging attention features are embedded into the model. Finally, to retain information about the sequence position, rotary position embedding is introduced to ensure robustness. We selected an authoritative Chinese dictionary and adopted a five-person annotation method to annotate multiple types of entities and concepts in the definitions. To validate the effectiveness of our enhanced model, experiments were conducted on the public dataset CADEC and our annotated Chinese dictionary dataset: on the CADEC dataset, with a slight decrease in recall rate, precision is improved by 2.78%, and the comprehensive metric F1 is increased by 0.89%; on the Chinese dictionary dataset, the precision is improved by 2.97%, the recall rate is increased by 2.35%, and the comprehensive metric F1 is improved by 2.66%.