3,459 research outputs found
Natural language processing
No full textBeginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Scalable Text Mining with Sparse Generative Models
No full textThe information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods.
This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines.
The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places
Adaptive text mining: Inferring structure from sequences
No full textText mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively
A modular architecture for systematic text categorisation
No full textThis work examines and attempts to overcome issues caused by the lack of formal standardisation when defining text categorisation techniques and detailing how they might be appropriately integrated with each other. Despite text categorisation’s long history the concept of automation is relatively new, coinciding with the evolution of computing technology and subsequent increase in quantity and availability of electronic textual data. Nevertheless insufficient descriptions of the diverse algorithms discovered have lead to an acknowledged ambiguity when trying to accurately replicate methods, which has made reliable comparative evaluations impossible.
Existing interpretations of general data mining and text categorisation methodologies are analysed in the first half of the thesis and common elements are extracted to create a distinct set of significant stages. Their possible interactions are logically determined and a unique universal architecture is generated that encapsulates all complexities and highlights the critical components. A variety of text related algorithms are also comprehensively surveyed and grouped according to which stage they belong in order to demonstrate how they can be mapped.
The second part reviews several open-source data mining applications, placing an emphasis on their ability to handle the proposed architecture, potential for expansion and text processing capabilities. Finding these inflexible and too elaborate to be readily adapted, designs for a novel framework are introduced that focus on rapid prototyping through lightweight customisations and reusable atomic components.
Being a consequence of inadequacies with existing options, a rudimentary implementation is realised along with a selection of text categorisation modules. Finally a series of experiments are conducted that validate the feasibility of the outlined methodology and importance of its composition, whilst also establishing the practicality of the framework for research purposes. The simplicity of experiments and results gathered clearly indicate the potential benefits that can be gained when a formalised approach is utilised
A text-mining system for extracting metabolic reactions from full-text articles
No full textBackground: Increasingly biological text mining research is focusing on the extraction of complex relationships
relevant to the construction and curation of biological networks and pathways. However, one important category of
pathway—metabolic pathways—has been largely neglected.
Here we present a relatively simple method for extracting metabolic reaction information from free text that scores
different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence
and location of stemmed keywords. This method extends an approach that has proved effective in the context of the
extraction of protein–protein interactions.
Results: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our
method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the
well-known protein-protein interaction extraction task.
Conclusions: We conclude that automated metabolic pathway construction is more tractable than has often been
assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed
Automatic Arabic Text Classification
No full textAutomated document classification is an important text mining task especially with the rapid growth of the number of online documents present in Arabic language. Text classification aims to automatically assign the text to a predefined category based on linguistic features. Such a process has different useful applications including, but not restricted to, e-mail spam detection, web page content filtering, and automatic message routing. This paper presents the results of experiments on document classification achieved on seven different Arabic corpora using statistical methodology. The performance of two popular classification algorithms in classifying the aforementioned corpora has been evaluated
The Role of Text Pre-processing in Sentiment Analysis
No full textIt is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature
Annotation of conceptual co-reference and text Mining the Qur'an
No full textThis research contributes to the area of corpus annotation and text mining by developing novel domain specific language resources. Most practical text mining applications restrict their domain. This research restricts the domain to the Qur'anic Text.
In this thesis, a number of pre-processing steps were undertaken and annotation information were added to the Qur'an. The raw Arabic Qur'an was pre-processed into morphological units using the Qur'anic Arabic Corpus (QAC). Qur'anic terms were indexed and converted into a vector space model using techniques in Information Retrieval (IR). In parallel, nearly 24,000 Qur'anic personal pronouns were annotated with information on their referents. These referents are consolidated and organized into a total of over 1,000 ontological concepts. Moreover, a dataset of nearly 8,000 pairs of related Qur'anic verses are compiled from books of scholarly commentary on the Qur'an. This vector space model, the pronoun tagging, the verse relatedness dataset, and the part-of-speech tags available in QAC all together served for a number of Qur'anic text mining applications which were rendered online for public use. Among these applications: lemma concordance, collocation, POS search of the Qur'an, verse similarity measures, concept clouds of a given verse, pronominal anaphora and Qur'anic chapter similarity.
Furthermore, machine learning experiments were conducted on automatic detection of verse similarity/relatedness as well as categorization of Qur'anic chapters based on their chronology of revelation. Domain specific linguistic features were investigated to induct learning algorithms. Results show that deep linguistic and world knowledge is needed to reach the human upper bound in certain computational tasks such as detecting text relatedness, question answering and textual entailment. However, many useful queries can be addressed using text mining techniques and layers of annotations made available through this research. The works presented here can be extended to include other similar texts like Hadith (i.e., saying of Prophet Muhammad), or other scriptures like the Gospels
Natural Language Processing and Text Mining (Turning Unstructured Data into Structured)
No full textThe integration of natural language processing (NLP) and text mining techniques has emerged as a key approach to harnessing the potential of unstructured clinical text data. This chapter discusses the challenges posed by clinical narratives and explores the need to transform them into structured formats for improved data accessibility and analysis. The chapter navigates through key concepts, including text pre-processing, text classification, text clustering, topic modeling, and advances in language models and transformers. It highlights the dynamic interplay between these techniques and their applications in tasks ranging from disease classification to extraction of side effects. In addition, the chapter acknowledges the importance of addressing bias and ensuring model explainability in the context of clinical prediction systems. By providing a comprehensive overview, the chapter offers insights into the synergy of NLP and text mining techniques in shaping the future of biomedical AI, ultimately leading to safer, more efficient, and more informed healthcare decisions
Natural language processing e tecniche semantiche per il supporto alla diagnosi: un esperimento
No full textLo studio si propone di realizzare la classificazione automatica di referti radiologici in due categorie, in base alla presenza o meno di una specifica patologia. Poichè i referti si presentano come testo non strutturato, è necessario estrarre features rilevanti dagli stessi attraverso un processo di Information Extraction. Tali features sono state ottenute mediante Natural Language Processing con GATE, un open source che permette di analizzare il testo e di inserire sullo stesso annotazioni con valenza semantica. Tali annotazioni sono state poi utilizzate come parametri in quattro algoritmi di machine learning, ottenendo la classificazione richiesta. Un confronto dei risultati ha permesso di valutare quale algoritmo, in questo specifico contesto e con le features considerate, ha ottenuto il miglior grado di accuratezz
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