Disambiguation Techniques Research Articles

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

Evaluating Word Sense Disambiguation Techniques for Punjabi Language: A Comparative Analysis

Word Sense Disambiguation (WSD) is a fundamental task in natural language processing (NLP) that focuses on determining the precise meaning of a word by analyzing its contextual usage.This paper presents a comprehensive analysis of various WSD techniques applied to the Punjabi language, including supervised, unsupervised, and knowledge-based methods. We compare the accuracy, performance, benefits, drawbacks, and resource requirements of these techniques.The study aims to provide a detailed overview of the state of WSD for Punjabi, with visual representations such as tables and graphs to illustrate comparative performance. Key Words: Word Sense Disambiguation, Punjabi Language, Natural Language Processing, Supervised Learning, Unsupervised Learning, Knowledge-Based Approach

Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. Keywords: Hybrid Genetic Algorithm, Trained Bayesian Ensemble, Short Messages Spam Detection Odiakaose, C. Emordi, F. Ejeh, P., Ashioba, N., Odeh, C., Attoh, O. & Azaka, M. (2024): Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 37-52. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P4

Abstract 17760: Conversion of Dicom ECG Images to Tabular Format for Building Large Language Model in Diagnoses and Disease Progression of Cardiovascular Conditions

Introduction: DICOM Electrocardiography (ECG) images from individuals are routinely stored at the institutional PACS server, including normal and abnormal findings. Hypothesis: The study’s objective is to create a database of ECGs as per Standard Communication Protocol (SCP) and to build an accurate Large Language Model (LLM) across different cardiovascular conditions Methods: DICOM ECGs are retrieved, anonymized and labelled to convert the signals to (x, y) coordinates with the help of PyDicom libraries. The tabular format is then stored with lead definition and dimensions of duration and amplitude, metadata, clinical conditions, and textual diagnoses. Results: In the pilot study, 1308 ECGs from individuals with different age (<45 & >45 years), gender (male & female), binary clinical categories (normal & abnormal) and heart rate (<90 & >90 per min) are selected for analysis and modeling. The DICOM image signal reproducibility following conversion (Figure 1 & 2) shows Cross Correlation Percentage of 0.85 (0.76 - 0.94) (Figure 3). An eXtreme Gradient Boosting (XGBoost) model (6) was trained to predict above variables. The results (Figure 4) show AUC highest for predicting binary clinical categories (normal vs abnormal) at 0.81 (0.7 - 0.9) and lowest for predicting age (<45 & >45 years) at 0.6 (0.46 - 0.7). The accuracies in the leads also show similar trend. The limitations include reproducibility of results in Lead VIII, IX and XI, which are sub average and hence being retrained with optimizers. These are initial results with relatively smaller database where multiclass disease or condition classifications are not performed. Conclusions: To conclude, the methodology possesses opportunities to improve the model with Deep Learning and Entity Disambiguation Techniques (NLP) to build Large Language ECG Models for cardiovascular diagnoses and disease progression trajectories.

Does it help to provide such posting content for interaction? An empirical study of physical and virtual goods branding

<p>The purpose of this study was to analyze several brands offering physical and virtual products to determine differences in content marketing, as well as the impact of content cues on brand awareness. An analytical framework examining the relationship between content messaging, cues, and positioning was also developed to investigate social media marketing and brand positioning. The study combines big data techniques, artificial intelligence and machine learning applications, data exploration, disambiguation, and model prediction techniques to observe public behavior and preferences. This study was intended to highlight the importance of functional cues (message recognition) and symbolic cues (positive emotional response) in brand communication, and the findings suggest that brands on social media platforms can use relevant messaging to stimulate brand awareness and facilitate user interaction. In summary, this study contributes to an understanding of key messaging characteristics of branding on social media, explores the differences in user behavior across brands, and documents the impact of cues on user engagement. The results demonstrate that advanced data analysis techniques can be used to extract representative keywords and determine their impact on brand interactions in the context of physical and virtual products. </p> <p> </p>

Partial multi-label learning via three-way decision-based tri-training

In real-world application scenarios, multi-label learning (MLL) datasets often contain some irrelevant noisy labels, which degrades the performance of traditional multi-label learning models. In order to deal with this problem, partial multi-label learning (PML) is proposed, in which each instance is associated with a candidate label set, which includes multiple relevant ground-truth labels and some irrelevant noisy labels. The common strategy to deal with this problem is disambiguating the candidate label set, but the co-occurrence of noisy labels and ground-truth labels makes the disambiguation technique susceptible to error. In this paper, a novel disambiguation-free PML approach named PML-TT is proposed. Specifically, by adapting the tri-training framework, mutual cooperation and iteration between classifiers are used to correct noisy labels and improve the performance of the learning model. Moreover, the three-way decision is adapted to solve the conflict problem of the base classifier and obtain more useful training samples. In addition, the precise supervisory information of the non-candidate labels is exploited to make the predictions of the base classifier more accurate. Finally, experimental results on both synthetic and real-world PML datasets show that the proposed PML-TT approach can effectively reduce the negative influence of noisy labels and learn a robust model.

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach.

Clinical Errors From Acronym Use in Electronic Health Record: A Review of NLP-Based Disambiguation Techniques

The adoption of Electronic Health Record (EHR) and other e-health infrastructures over the years has been characterized by an increase in medical errors. This is primarily a result of the widespread usage of medical acronyms and abbreviations with multiple possible senses (i.e., ambiguous acronyms). The advent of Artificial Intelligence (AI) technology, specifically Natural Language Processing (NLP), has presented a promising avenue for tackling the intricate issue of automatic sense resolution of acronyms. Notably, the application of Machine Learning (ML) techniques has proven to be highly effective in the development of systems aimed at this objective, garnering significant attention and interest within the research and industry domains in recent years. The significance of automating the resolution of medical acronym senses cannot be overstated, especially in the context of modern healthcare delivery with the widespread use of EHR. However, it is disheartening to note that comprehensive studies examining the global adoption of EHR, assessing the impact of acronym usage on medical errors within EHR systems, and reporting on the latest trends and advancements in ML-based NLP solutions for disambiguating medical acronyms remain severely limited. In this current study, we present a detailed overview on medical error, its origins, unintended effects, and EHR-related errors as a subclass of clinical error. Furthermore, this paper investigates the adoption of EHR systems in developed and developing nations, as well as the review concludes with an examination of various artificial intelligence techniques, particularly machine learning algorithms for medical acronym and abbreviation disambiguation in EHRs.

A dataset for evaluating Bengali word sense disambiguation techniques

A dataset for evaluating Bengali word sense disambiguation techniques

Translation Solutions for Dealing With Ambiguity in Alice’s Adventures in Wonderland

"Translation solutions for dealing with ambiguity in Alice’s Adventures in Wonderland. This paper shows how different types of ambiguity embedded in the matrix of Lewis Carroll’s Alice’s Adventures in Wonderland (the 1993 edition) are dealt with in two prestigious Romanian translations – Frida Papadache’s Peripeţiile Alisei în Ţara Minunilor (1976) and Antoaneta Ralian’s Alice în Ţara Minunilor (2007) – as a tribute to the international appeal of Alice. My focal aim is to present a comparative analysis of the methods employed in translating Carroll’s equivocal lexical items, which make it increasingly difficult to match grammatical categories with function. This paper also aims at describing disambiguation techniques applied primarily in determining if the two translators managed to reinforce the original textual leeway at their disposal in the pure spirit of Carroll. My analysis relies heavily on Dirk Delabastita’s translation strategies as precautionary measures to cope with Carroll’s specialized type of literary discourse. The findings submitted by this paper are consistent with the idea that translating Carroll’s craft unavoidably entails a partial loss of meaning, brought about by the yawning gap between the intended message and interpretation, which can result in either overtranslation or undertranslation. The extensive use of double-entendre in the source-text cannot be recoded entirely in the target language, despite the translators’ excellent command of English. Keywords: Carrollian humor; ambiguity; translation solutions; disambiguation techniques; textual challenges "

A novel word sense disambiguation approach using WordNet knowledge graph

Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human’s natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document’s main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms’ order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.

Tree-KGQA: An Unsupervised Approach for Question Answering Over Knowledge Graphs

Most Knowledge Graph-based Question Answering (KGQA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tree-KGQA</b> , an unsupervised KGQA system leveraging pre-trained language models and tree-based algorithms. Entity and relation linking are essential components of any KGQA system. We employ several pre-trained language models in the entity linking task to recognize the entities mentioned in the question and obtain the contextual representation for indexing. Furthermore, for relation linking we incorporate a pre-trained language model previously trained for language inference task. Finally, we introduce a novel algorithm for extracting the answer entities from a KG, where we construct a forest of interpretations and introduce tree-walking and tree disambiguation techniques. Our algorithm uses the linked relation and predicts the tree branches that eventually lead to the potential answer entities. The proposed method achieves 4.5% and 7.1% gains in F1 score in entity linking tasks on LC-QuAD 2.0 and LC-QuAD 2.0 (KBpearl) datasets, respectively, and a 5.4% increase in the relation linking task on LC-QuAD 2.0 (KBpearl). The comprehensive evaluations demonstrate that our unsupervised KGQA approach outperforms other supervised state-of-the-art methods on the WebQSP-WD test set (1.4% increase in F1 score) - without training on the target dataset.

Word Sense Disambiguation and Semantics techniques

Of the many kinds of ambiguity in language is word sense. This problem had received the most attention in computational linguistics, and the reasons for this are clear: their resolution is seemingly essential for any practical application, and they seem to require a wide variety of methods and knowledge-sources with no pattern apparent in what any particular instance requires.Since the beginning of NLP field and the researchers trying to bypass the bottleneck of word sense disambiguation (WSD). The lack of high-performing methods for sense disambiguation may be considered the major obstacle that prevented an extensive use of natural language processing techniques in many areas of information technology, such as information classification and retrieval, query processing, advanced Web search, document warehousing, etc.This obstacle inspired many researchers to come up with new methods, approaches, and to exploit whatever knowledge resources exist to develop a solution. Previously most of the techniques used were based on a huge amount of knowledge sources, which suffers from the problems of incomplete, inadequate, and expensive resources.After that a trained techniques and the statistical approaches dominant over the field for a while were at the beginning good results obtained with limited and small domains. But with the expanding of the domains, the luck of good trained knowledge sources besides the neglecting of the semantic roles did not lay good results at the end.Now a day’s the research had been directed towards a hybrid approach that depends on the available and more reliable knowledge sources besides the semantic techniques, and training methods which consider the role of the words in its context.In this research we will take a case study of WSD. Then we will display the semantic techniques used in it and display the effect of implementing consistence concept evaluation method to produce more reliable results based on semantic similarity, besides that we will show how more conceptualization of the semantic relations can be implemented through semantic information.

Impact of Entity Disambiguation Errors on Social Network Properties

Entities in social networks may be subject to consolidation when they are inconsistently indexed, and subject to splitting when multiple entities share the same name. How much do errors or shortfalls in entity disambiguation distort network properties? We show empirically how network analysis results and derived implications can tremendously change depending solely on entity resolution techniques. We present a series of controlled experiments where we vary disambiguation accuracy to study error propagation and the robustness of common network metrics, topologies and key players. Our results suggest that for email data, not conducting deduplication, e.g. when operating on the level of email addressed instead of individuals, can make organizational communication networks appear to be less coherent and integrated as well as bigger than they truly are. For co-publishing networks, improper merging as caused by the commonly used initial based disambiguation techniques can make a scientific sector seem more dense and cohesive than it really is, and individual authors appear to be more productive, collaborative and diversified than they actually are. Disambiguation errors can also lead to the false detection of power law distributions of node degree; suggesting preferential attachment processes that might not apply.

A quantitative view of the structure of institutional scientific collaborations using the example of Berlin

Abstract This paper examines the structure of scientific collaborations in Berlin as a specific case with a unique history of division and reunification. It aims to identify strategic organizational coalitions in a context with high sectoral diversity. We use publications data with at least one organization located in Berlin from 1996–2017 and their collaborators worldwide. We further investigate four members of the Berlin University Alliance (BUA), as a formerly established coalition in the region, through their self-represented research profiles compared with empirical results. Using a bipartite network modeling framework, we move beyond the uncontested trend towards team science and increasing internationalization. Our results show that BUA members shape the structure of scientific collaborations in the region. However, they are not collaborating cohesively in all fields and there are many smaller scientific actors involved in more internationalized collaborations in the region. Larger divides exist in some fields. Only Medical and Health Sciences have cohesive intraregional collaborations, which signals the success of the regional cooperation established in 2003. We explain possible underlying factors shaping the intraregional groupings and potential implications for regions worldwide. A major methodological contribution of this paper is evaluating the coverage and accuracy of different organization name disambiguation techniques.

Doppler disambiguation in MIMO FMCW radars with binary phase modulation

Multiple-input multiple-output arrays in frequency modulated continuous wave (FMCW) radars provide the angular resolution required to isolate objects in complex automotive scenes. However, the demodulation of individual antenna contributions increases the chirp repetition interval, which reduces the maximum unambiguously measurable velocity. Objects moving faster than this velocity are then incorrectly folded in the range-Doppler maps, leading to critical inaccuracies in the advanced driver assistance systems. Here, this problem is addressed from the binary phase modulation (BPM) perspective, which is a scheme particularly attractive due to its high signal-to-noise ratio, and whose intrinsic phase alterations prevent traditional disambiguation techniques from being reliable. The authors present a detailed analysis on the design, implementation, and validation of Doppler disambiguation techniques for BPM systems. The presented benchmark includes a variety of algorithms involving the Chinese remainder theorem, density-based spatial clustering of applications with noise (DBSCAN), hypothetical phase compensation, and an intuitive range-based approach. The techniques are extensively validated using synthetic data generated with MATLAB, and real data collected with a 77-GHz FMCW radar. The results show the best set of trade-offs for the enhanced DBSCAN (EDBSCAN) method in terms of robustness, computational overhead, velocity span, and disambiguation rate.

Efficient estimation of Hindi WSD with distributed word representation in vector space

Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language text. However, these models do not provide good results for low-resource languages, due to the lack of labelled and tagged data. Therefore, in this work, we have examined different word embedding techniques for word sense disambiguation of the Hindi language texts. Several studies in the literature show that these embeddings have been utilized for different foreign languages in the field of word sense disambiguation. However, to the best of our knowledge, no such work exists for the Hindi language. Therefore, in this paper, we utilize various existing word embeddings for WSD of Hindi text. Moreover, we have created Hindi word embeddings on articles taken from Wikipedia and test the quality of the created word embeddings using Pearson correlation. In this direction, we perform different experiments and observe that Word2Vec model gives best performance among all the considered embeddings on the used Hindi dataset. In our method, the proposed model directly takes input that is trained with word embedding methods and helps to develop a sense inventory using clustering that has been employed for performing disambiguation. Experimental observations indicate that the performance of the proposed approach is moderate and competent in terms of accuracy. The paper, thus, presents how WSD can leverage these representations to encode rich semantic information.

Unsupervised Predominant Sense Detection and Its Application to Text Classification

This paper focuses on the domain-specific senses of words and proposes a method for detecting predominant sense depending on each domain. Our Domain-Specific Senses (DSS) model is an unsupervised manner and detects predominant senses in each domain. We apply a simple Markov Random Walk (MRW) model to ranking senses for each domain. It decides the importance of a sense within a graph by using the similarity of senses. The similarity of senses is obtained by using distributional representations of words from gloss texts in the thesaurus. It can capture large semantic context and thus does not require manual annotation of sense-tagged data. We used the Reuters corpus and the WordNet in the experiments. We applied the results of domain-specific senses to text classification and examined how DSS affects the overall performance of the text classification task. We compared our DSS model with one of the word sense disambiguation techniques (WSD), Context2vec, and the results demonstrate our domain-specific sense approach gains 0.053 F1 improvement on average over the WSD approach.

Author Name Disambiguation Techniques for Academic Literature: A Review

Author Name Disambiguation Techniques for Academic Literature: A Review

Lexical Ambiguity in Arabic Information Retrieval: The Case of Six Web-Based Search Engines

In recent years, both research and industry have shown an increasing interest in developing reliable information retrieval (IR) systems that can effectively address the growing demands of users worldwide. In spite of the relative success of IR systems in addressing the needs of users and even adapting to their environments, many problems remain unresolved. One main problem is lexical ambiguity which has negative impacts on the performance and reliability of IR systems. To date, lexical ambiguity has been one of the most frequently reported problems in the Arabic IR systems despite the development of different word sense disambiguation (WSD) techniques. This is largely attributed to the limitations of such techniques in addressing the issue of linguistic peculiarities. Hence, this study addresses these limitations by exploring the reasons for lexical ambiguity in IR applications in Arabic as one step towards reliable and practical solutions. For this purpose, the performances of six search engines Google, Bing, Baidu, Yahoo, Yandex, and Ask are evaluated. Results indicate that lexical ambiguities in Arabic IR applications are mainly due to the unique morphological and orthographic system of the Arabic language, in addition to its diglossia and the multiple colloquial dialects where sometimes mutual intelligibility is not achieved. For better disambiguation and IR performances in Arabic, this study proposes that clustering models based on supervised machine learning theory should be trained to address the morphological diversity of Arabic and its unique orthographic system. Search engines should also be adapted to the geographic location of the users in order to address the issue of vernacular dialects of Arabic. They should also be trained to automatically identify the different dialects. Finally, search engines should consider all varieties of Arabic and be able to interpret the queries regardless of the particular language adopted by the user.