Search Results (74)

Part-of-speech (POS) tagging in Arabic is hard because its rich root-and-pattern morphology and the absence of short vowels make one unvoweled string compatible with many categories. This paper presents Jawhar, a hybrid framework that couples a high-performance morphological analyser with contextual reranking using a pretrained Arabic language model. Jawhar is an autonomous engine inspired by Al-Khalil MorphoSys and rebuilt in Python that replaces the original XML databases with optimised JSON structures for faster inference. It enumerates the morphologically valid candidates of each token, and a CAMeL-BERT stage then scores each candidate by its full morphological signature (type, POS, root, pattern, and voweled form). On the Prague Arabic Dependency Treebank, mapped to the universal 17-tag POS scheme, the fine-tuned scorer reached 96.4% token accuracy (macro-F1 0.921), on par with published neural taggers, while a candidate-constrained hybrid attached a full morphological analysis to 65.9% of tokens at the same accuracy and reached a 97.4% oracle ceiling. A rule-based configuration reached 54.4%, and the zero-shot reranker reached parity (54.3%), which showed that within a fixed candidate set, reordering could not cross the coverage ceiling. The main contribution was a token-level decomposition of the error budget that isolated candidate coverage and label mapping from contextual ranking, released with a public analyser and harness. Full article

Part-of-Speech (POS) tagging is a foundational task in Natural Language Processing (NLP), yet it remains challenging for low-resource and morphologically rich languages such as Amazigh. This paper proposes a hybrid ensemble framework for Amazigh POS tagging that integrates three complementary models: a Bidirectional Long Short-Term Memory network (BiLSTM), a Conditional Random Field model (CRF), and a rule-based morphological analyzer (RBMA). Rather than treating prior results obtained on different corpora and tag inventories as directly comparable, the study evaluates all proposed components under a common 54-tag experimental setting based on the publicly available Amazigh Linguistic Dataset. Three ensemble strategies are examined: majority voting, validation-weighted voting, and logistic-regression stacking. An additional late-fusion ablation applies hard and soft RBMA constraints to CRF and Stacking outputs; hard masking degrades performance substantially, whereas soft masking is more robust but remains below unconstrained decoding. The best micro-level performance is obtained by the stacking ensemble, which reaches 98.51% Micro-F1/accuracy, whereas the boosting-like weighted ensemble obtains the strongest Macro-F1 among the ensemble variants, reaching 74.24%. These results show that hybrid ensemble methods can improve token-level accuracy in low-resource POS tagging, while also revealing a trade-off between frequent-tag accuracy and rare-tag robustness. The findings highlight the usefulness of combining neural, probabilistic, and rule-based information for Amazigh POS tagging, and point to class-balanced meta-learning and character/subword representations as important directions for improving rare and out-of-vocabulary categories. Full article

Words occurring in similar contexts have been observed to have similar meanings. A natural and established method within computational linguistics implements this observation by representing words as vectors with dimensions determined by words that are witnessed in fixed positions in relation to the target word. We generalize this context vector approach to part-of-speech (POS) sequences appropriate to word sequences. As with words, the context of a POS tag (considering the POS tags occurring before and after any target tag) reflects its syntactic constraints and may approximate the “meaning” of the target tag, from a distributional perspective. We use the 111-million-word British National Corpus (BNC) and the sequence of POS labels lifted from those texts to calculate POS context vectors. We observed significant agreement between the clusters of POS context vectors and the supercategories of corresponding POS tags, and examined potential categorization of the POS categories that emerged from the vector clusters. We also found that though vector measures partially align with the predictions of generativist linguistic theories, the approach suggests a more complex relation between syntactic categories. We conclude that a mutual-information-based approach better approximates the distributional “meaning” of syntactic categories than the conditional probability distribution of POS symbols. Full article

Chinese cloze question generation for educational assessments requires identifying gap phrases that accurately reflect key knowledge points, posing significant challenges to automated systems. We observe that the syntactic boundaries revealed by part-of-speech (POS) tags closely align with the semantic boundaries of target gap phrases. Motivated by this observation, we propose a multi-task learning framework in which gap phrase identification serves as the primary task and POS tagging as a complementary auxiliary task. The two tasks share a common BERT-BiLSTM encoder, enabling mutual reinforcement of both syntactic and semantic representations through joint training. To further capture the interaction between label semantics and contextual word representations, we introduce a label-attention mechanism that models dependencies between the global word sequence and candidate label embeddings. Additionally, we construct a refined POS tag subset by excluding categories whose boundaries show no alignment with gap phrase boundaries, thereby strengthening the correspondence between the two tasks. Evaluated on a real-world dataset of 20.5K questions spanning five academic disciplines, our method achieves an F1 score of 65.85%, with a Recall of 67.79%, representing improvements of 2.12% and 4.35% over the prior state-of-the-art, respectively. These results demonstrate that exploiting the alignment between syntactic and semantic structures through joint learning is effective for generating educationally meaningful fill-in-the-blank questions. Full article

Kazakh sequence labeling is constrained by limited annotated resources, while its agglutinative morphology and productive suffixation increase data sparsity and exacerbate label inconsistency in part-of-speech (POS) tagging and named entity recognition (NER). This paper proposes an LLM-assisted weak supervision framework in which a large language model generates synthetic token-level annotations that are subsequently filtered using confidence-based criteria and combined with a smaller manually verified subset to train Transformer-based sequence taggers with Conditional Random Field (CRF) decoding. The pipeline unifies corpus construction, weak-label generation, quality filtering, word-to-subword alignment, and CRF-refined structured prediction into a reproducible workflow. Experimental results show that contextual encoders and structured decoding provide strong performance for Kazakh POS and NER, while the proposed training design enables efficient convergence with diminishing returns beyond moderate epoch budgets. Error-slice analysis indicates that residual errors are concentrated in rare tokens, morphologically complex long words, longer sentences, and the ORG entity class. Overall, the findings support the use of LLM-assisted weak supervision as a scalable strategy for low-resource Kazakh sequence labeling when synthetic labels are controlled through filtering and refined by structured decoding. Full article

Named Entity Recognition (NER) plays a crucial role in Aspect-Based Sentiment Identification (ABSI), enabling the extraction of domain-specific aspects and their associated sentiment expressions from unstructured textual reviews. In complex domains such as movie reviews, sentiment is frequently conveyed through references to named entities (e.g., actors, directors, or movie titles) and other contextual cues. However, many existing ABSI approaches treat NER as a separate preprocessing step, limiting the effective modeling of entity–aspect–opinion relationships. Integrating NER directly into the ABSI framework, allows entity-specific opinions to be more accurately identified, overlapping aspects to be disambiguated, and contextual sentiment expressions to be captured more effectively. To address these challenges, this study proposes an integrated NER-based aspect identification model built on feature-enhanced LSTM and BiLSTM architectures. Linguistic features, including Parts-of-Speech (POS) tags and chunking information, are incorporated to enrich contextual representations, while a Conditional Random Field (CRF) decoding layer models inter-label dependencies for coherent sequence-level predictions of named entities, aspects, and associated opinion expressions. Compared with large transformer-based models, the proposed BiLSTM-CRF architecture offers lower computational complexity, fewer parameters, and allows explicit integration and analysis of linguistic features that are often implicitly encoded in transformer attention mechanisms. The model is evaluated through multiple experimental variants across three domains. Four configurations are applied to movie-review data to jointly extract person names, movie titles, and aspect-opinion pairs, while six configurations assess cross-domain robustness on restaurant and laptop review datasets. Results show that the BiLSTM-CRF model augmented with POS features consistently outperforms baseline configurations in the movie domain and remains competitive across domains, achieving an F1-score of 0.89. These findings demonstrate that explicit linguistic feature integration within a CRF-based sequence modeling can provide an effective and computationally efficient alternative to large-scale transformer fine-tuning for structured, entity-linked ABSI tasks. Full article

Most pre-trained language representation models are designed to encode contextualized semantic information for general language processing tasks. However, they are insufficient for math word problem (MWP) solving, which requires not only linguistic syntax and semantic understanding but also numerical reasoning. In this work, we introduce SSN4Solver, a deep neural solver that improves MWP-solving performance by symmetrically fusing syntax, semantics, and numeracy representations within its contextual encoder. Our approach jointly captures syntactic structures from dependency trees, semantic features from part-of-speech tags, and the attributes and relations of numerical entities. By treating these heterogeneous information sources in a balanced and aligned manner, SSN4Solver constructs a rich, multi-faceted representation for MWP solving without introducing substantial computational overhead, empowering human–computer interaction (HCI) applications such as adaptive educational interfaces and intelligent tutoring systems. Extensive experiments demonstrate that SSN4Solver outperforms existing baseline models. In addition, a visualization scheme is designed to elucidate how the three types of representations contribute to the solving process. SSN4Solver thus offers a scalable solution, contributing to the development of HCI systems that are both intelligent and mathematically effective. Full article

Aspect Sentiment Triplet Extraction (ASTE) aims to jointly identify aspect terms, opinion terms, and their associated sentiment polarities. Existing approaches, such as tagging or span-based modeling, often struggle with complex aspect–opinion interactions and long-distance dependencies. We propose a Knowledge-Augmented Graph Convolutional Network (KMG-GCN) that represents a sentence as a multi-channel graph integrating syntactic dependencies, part-of-speech tags, and positional relations. An adjacency tensor is constructed via a biaffine attention mechanism, while a multi-anchor triplet learning strategy with orthogonal projection enhances representation disentanglement. Furthermore, a pairwise refinement module explicitly models aspect–opinion associations, improving robustness against overlapping triplets. Experiments on multiple benchmarks demonstrate that KMG-GCN achieves state-of-the-art performance with improved efficiency and generalization. Full article

Metaphor is ubiquitous in daily communication and makes language expression more vivid. Identifying metaphorical words, known as metaphor detection, is crucial for capturing the real meaning of a sentence. As an important step of metaphorical understanding, the correct interpretation of metaphorical words directly affects metaphor detection. This article investigates how to use metaphor interpretation to enhance metaphor detection. Since previous approaches for metaphor interpretation are coarse-grained or constrained by ambiguous meanings of substitute words, we propose a different interpretation mechanism that explains metaphorical words by means of gloss-based interpretations. To comprehensively explore the optimal joint strategy, we go beyond previous work by designing diverse model architectures. We investigate both classification and sequence labeling paradigms, incorporating distinct component designs based on MIP and SPV theories. Furthermore, we integrate Part-of-Speech tags and external knowledge to further refine the feature representation. All methods utilize pre-trained language models to encode text and capture semantic information of the text. Since this mechanism involves both metaphor detection and metaphor interpretation but there is a lack of datasets annotated for both tasks, we have enhanced three datasets with glosses for metaphor detection: one Chinese dataset (PSUCMC) and two English datasets (TroFi and VUA). Experimental results demonstrate that the proposed joint methods are superior to or at least comparable to state-of-the-art methods on the three enhanced datasets. Results confirm that joint learning of metaphor detection and gloss-based interpretation makes metaphor detection more accurate. Full article

In the context of the BiblIndex project, which is an online index of biblical textual reuses by the Church Fathers, intrabiblical intertextuality must be considered to better understand the underlying basis of the Church Fathers’ thought. This paper examines the reuse of Psalmic texts in the New Testament as a test case for experimenting with a detection tool that uses traditional natural language processing (NLP) methods exclusively. Biblical verses are compared using similarity measures based on various NLP operations, such as tokenization, lemmatization, part-of-speech tagging, stop word filtering and synset assignment. Textometric measures provide a framework for the numerical assessment of grammatical, lexical and semantic similarities between textual units. We demonstrate the efficiency of this reproducible method, which does not involve a ‘black box’ effect, for detecting and characterizing literal quotations and a significant range of echoes. Full article

The performance of extractive summarization models is often limited by their dependence on human references that may contain inaccuracies or subjective biases. Existing methods typically rely solely on sentence-level supervision, which lacks explicit grounding in the actual semantic content of the source document, thus limiting their robustness. We propose SSF-KW, a novel multi-task learning framework that enhances robustness by jointly optimizing keyword extraction and sentence selection. Our approach is designed to explicitly anchor salience decisions in the document’s intrinsic semantic structure, reducing reliance on potentially noisy labels. To this end, the model employs a shared BERT encoder to represent sentences, and identifies keywords through part-of-speech tagging, semantic similarity analysis, and fine-grained keyword signals with sentence-level representations via a transformer-based fusion module. The entire framework is optimized with a combined loss function that balances both tasks. Comprehensive evaluations on CNN/DailyMail, XSum, and WikiHow demonstrate that SSF-KW consistently outperforms baselines ROUGE-1 scores of 43.27, 25.43, and 30.03, respectively. Ablation studies confirm the contribution of each component, with the word-level module proving especially critical for capturing key concepts in procedural texts like WikiHow. Full article

Arabic natural language processing (NLP) has garnered significant attention in recent years due to the growing demand for automated text and Arabic-based intelligent systems, in addition to digital transformation in the Arab world. However, the unique linguistic characteristics of Arabic, including its rich morphology, diverse dialects, and complex syntax, pose significant challenges to NLP researchers. This paper provides a comprehensive review of the main linguistic challenges inherent in Arabic NLP, such as morphological complexity, diacritics and orthography issues, ambiguity, and dataset limitations. Furthermore, it surveys the major computational techniques employed in tokenisation and normalisation, named entity recognition, part-of-speech tagging, sentiment analysis, text classification, summarisation, question answering, and machine translation. In addition, it discusses the rapid rise of large language models and their transformative impact on Arabic NLP. Full article

Part-of-speech (POS) tagging in low-resource, morphologically rich languages (LRLs/MRLs) remains challenging due to extensive affixation, high out-of-vocabulary (OOV) rates, and pervasive polysemy. We propose MRL-POS, a unified Transformer-CRF framework that dynamically selects informative affix features and integrates them with deep contextual embeddings via a novel dual-gating co-attention mechanism. First, a Dynamic Affix Selector adaptively adjusts n-gram ranges and frequency thresholds based on word length to ensure high-precision affix segmentation. Second, the Affix–Context Co-Attention Module employs two gating functions that conditionally amplify contextual dimensions with affix cues and vice versa, enabling robust disambiguation of complex and ambiguous forms. Third, Layer-Wise Attention Pooling aggregates multi-layer XLM-RoBERTa representations, emphasizing those most relevant for morphological and syntactic tagging. Evaluations on Uyghur, Kyrgyz, and Uzbek show that MRL-POS achieves an average F₁ of 84.10%, OOV accuracy of 84.24%, and Poly-F₁ of 72.14%, outperforming strong baselines by up to 8 F₁ points. By explicitly modeling the symmetry between morphological affix cues and sentence-level context through a dual-gating co-attention mechanism, MRL-POS achieves a balanced fusion that both preserves local structure and captures global dependencies. Interpretability analyses confirm that 89.1% of the selected affixes align with linguistic expectations. This symmetric design not only enhances robustness in low-resource and agglutinative settings but also offers a general paradigm for symmetry-aware sequence labeling tasks. Full article

Loess landslide disasters are influenced by a multitude of factors, including slope conditions, triggering mechanisms, and spatial attributes. Extracting these factors from unstructured geological texts is challenging due to nested entities, semantic ambiguity, and rare domain-specific terms. This study proposes a joint extraction framework guided by a domain ontology that categorizes six types of loess landslide influencing factors, including spatial relationships. The ontology facilitates conceptual classification and semi-automatic nested entity annotation, enabling the construction of a high-quality corpus with eight tag types. The model integrates a Soft-Lexicon mechanism that enhances character-level GloVe embeddings with explicit lexical features, including domain terms, part-of-speech tags, and word boundary indicators derived from a domain-specific lexicon. The resulting hybrid character-level representations are then fed into a BiLSTM-CRF architecture to jointly extract entities, attributes, and multi-level spatial and causal relationships. Extracted results are structured using a content-knowledge model to build a spatially enriched knowledge graph, supporting semantic queries and intelligent reasoning. Experimental results demonstrate improved performance over baseline methods, showcasing the framework’s effectiveness in geohazard information extraction and disaster risk analysis. Full article

Customers heavily depend on reviews for product information. Fake reviews may influence the perception of product quality, making online reviews less effective. ChatGPT’s (GPT-3.5 and GPT-4) ability to generate human-like reviews and responses to inquiries across several disciplines has increased recently. This leads to an increase in the number of reviewers and applications using ChatGPT to create fake reviews. Consequently, the detection of fake reviews generated or rephrased by ChatGPT has become essential. This paper proposes a new approach that distinguishes ChatGPT-rephrased reviews, considered fake, from real ones, utilizing a balanced dataset to analyze the sentiment and linguistic patterns that characterize both reviews. The proposed model further leverages Explainable Artificial Intelligence (XAI) techniques, including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) for deeper insights into the model’s predictions and the classification logic. The proposed model performs a pre-processing phase that includes part-of-speech (POS) tagging, word lemmatization, tokenization, and then fine-tuned Transformer-based Machine Learning (ML) model DistilBERT for predictions. The obtained experimental results indicate that the proposed fine-tuned DistilBERT, utilizing the constructed balanced dataset along with a pre-processing phase, outperforms other state-of-the-art methods for detecting ChatGPT-rephrased reviews, achieving an accuracy of 97.25% and F1-score of 97.56%. The use of LIME and SHAP techniques not only enhanced the model’s interpretability, but also offered valuable insights into the key factors that affect the differentiation of genuine reviews from ChatGPT-rephrased ones. According to XAI, ChatGPT’s writing style is polite, uses grammatical structure, lacks specific descriptions and information in reviews, uses fancy words, is impersonal, and has deficiencies in emotional expression. These findings emphasize the effectiveness and reliability of the proposed approach. Full article