Explicit semantic path mining via wikipedia knowledge tree

Text mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemming and lemmatization, tagging and annotation, deriving knowledge patterns, evaluating and interpreting the results. There are numerous approaches for performing text mining tasks, like: clustering, categorization, sentimental analysis, and summarization. There is a growing need to standardize the evaluation of these tasks. One major component of establishing standardization is to provide standard datasets for these tasks. Although there are various standard datasets available for traditional text mining tasks, but there are very few and expensive datasets for blog-mining task. Blogs, a new genre in web 2.0 is a digital diary of web user, which has chronological entries and contains a lot of useful knowledge, thus offers a lot of challenges and opportunities for text mining. In this paper, we report a new indigenous dataset for Pakistani Political Blogosphere. The paper describes the process of data collection, organization, and standardization. We have used this dataset for carrying out various text mining tasks for blogosphere, like: blog-search, political sentiments analysis and tracking, identification of influential blogger, and clustering of the blog-posts. We wish to offer this dataset free for others who aspire to pursue further in this domain.

Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives--statistics, data mining, linguistics, and information retrieval--and provides readers with the means to successfully complete text mining tasks on their own. The book begins with an introduction to regular expressions, a text pattern methodology, and quantitative text summaries, all of which are fundamental tools of analyzing text. Then, it builds upon this foundation to explore: Probability and texts, including the bag-of-words model Information retrieval techniques such as the TF-IDF similarity measure Concordance lines and corpus linguistics Multivariate techniques such as correlation, principal components analysis, and clustering Perl modules, German, and permutation tests Each chapter is devoted to a single key topic, and the author carefully and thoughtfully introduces mathematical concepts as they arise, allowing readers to learn as they go without having to refer to additional books. The inclusion of numerous exercises and worked-out examples further complements the book's student-friendly format. Practical Text Mining with Perl is ideal as a textbook for undergraduate and graduate courses in text mining and as a reference for a variety of professionals who are interested in extracting information from text documents.

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation.In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences.The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches.

Reducing explicit semantic representation vectors using Latent Dirichlet Allocation

Innovations in science and technology boost-up the generation of an immense volume of digital data in the form of text, audio, video, images and paved the way for a new era called Big Data. A majority of this data is generated as a semi-structured and unstructured format poses a challenge for Natural Language Processing (NLP) and text analytics. This chapter seeks to layout the text mining perspective of big data analytics with an emphasis on applications in biomedicine and healthcare. The chapter aims to provide a better understanding of areas such as Big Data, Text Mining, Data Mining, Natural Language Processing and Machine Learning and its inter linkage. The chapter illustrates phases and task of text mining in big data scope. Finally, the chapter ends with a description of the two application areas biomedicine and healthcare where text mining using big data analytics will be used.

Since its debut the Explicit Semantic Analysis (ESA) has received much attention in the IR community. ESA has been proven to perform surprisingly well in several tasks and in different contexts. However, given the conceptual motivation for ESA, recent work has observed unexpected behavior. In this paper we look at the foundations of ESA from a theoretical point of view and employ a general probabilistic model for term weights which reveals how ESA actually works. Based on this model we explain some of the phenomena that have been observed in previous work and support our findings with new experiments. Moreover, we provide a theoretical grounding on how the size and the composition of the index collection affect the ESA-based computation of similarity values for texts.

Stemming is a technique used to reduce inflected and derived words to their basic forms (stem or root). It is a very important step of pre-processing in text mining, and generally used in many areas of research such as: Natural language Processing NLP, Text Categorization TC, Text Summarizing TS, Information Retrieval IR, and other tasks in text mining. Stemming is frequently useful in text categorization to reduce the size of terms vocabulary, and in information retrieval to improve the search effectiveness and then gives us relevant results. In this paper, we propose a new multilingual stemmer based on the extraction of word root and in which we use the technique of n-grams. We validated our stemmer on three languages which are: Arabic, French and English.

The significance of natural language texts as the prime information structure for the management and dissemination of knowledge in organisations is still increasing. Making relevant documents available depending on varying tasks in different contexts is of primary importance for any efficient task completion. Implementing this demand requires the content based processing of texts, which enables to reconstruct or, if necessary, to explore the relationship of task, context and document. Text mining is a technology that is suitable for solving problems of this kind. In the following, semiotic aspects of text mining are investigated. Based on the primary object of text mining - natural language lexis - the specific complexity of this class of signs is outlined and requirements for the implementation of text mining procedures are derived. This is done with reference to text linkage introduced as a special task in text mining. Text linkage refers to the exploration of implicit, content based relations of texts (and their annotation as typed links in corpora possibly organised as hypertexts). In this context, the term systemic functional hypertext is introduced, which distinguishes genre and register layers for the management of links in a poly-level hypertext system.

Cross-document knowledge discovery is dedicated to exploring meaningful (but maybe unapparent) information from a large volume of textual data. The sparsity and high dimensionality of text data present great challenges for representing the semantics of natural language. Our previously introduced Concept Chain Queries (CCQ) was specifically designed to discover semantic relationships between two concepts across documents where relationships found reveal semantic paths linking two concepts across multiple text units. However, answering such queries only employed the Bag of Words (BOW) representation in our previous solution, and therefore terms not appearing in the text literally are not taken into consideration. Explicit Semantic Analysis (ESA) is a novel method proposed to represent the meaning of texts in a higher dimensional space of concepts which are derived from large-scale human built repositories such as Wikipedia. In this paper, we propose to integrate the ESA technique into our query processing, which is capable of using vast knowledge from Wikipedia to complement existing information from text corpus and alleviate the limitations resulted from the BOW representation. The experiments demonstrate the search quality has been greatly improved when incorporating ESA into answering CCQ, compared with using a BOW-based approach.KeywordsKnowledge DiscoverySemantic RelatednessCross-Document Knowledge DiscoveryDocument Representation

Our goal is to support users who want to discover or create knowledge from a large amount of text data. Text mining is a process that extracts novel knowledge from unstructured data. A variety of applications for text mining, such as Total Environment for Text Data Mining, have been proposed. However, to the best of our knowledge, these methods are not effective at conducting text mining tasks aimed at finding novel knowledge. In this paper, we discuss characteristics of the text mining process and propose a design principle for building text mining applications based on two concepts: (1) text mining is an exploratory search task, and (2) text mining is a process for creative knowledge work.

SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis

Event relation extraction is a fundamental task in text mining, which has wide applications in event-centric natural language processing. However, most of the existing approaches can hardly model complicated contexts since they fail to use dependency-type knowledge in texts to assist in identifying implicit clues to event relations, leading to the sub-optimal performance on this task. To this end, we propose a novel type-guided attentive graph convolutional network for event relation extraction. Specifically, given the input text, the event-specific syntactic dependency graph is first constructed, from which both the local and global dependency knowledge related to events are derived. Then, a dependency-type guided attentive graph convolutional network is designed for learning representations of events, in which the local and global dependency information are utilized to effectively aggregate semantic context among texts. Finally, the event relation is predicted based on the representations of event pair and the representation of the whole text, completing the task of event relation extraction. The experimental results on multiple datasets show that our method significantly outperforms the state-of-the-art baselines. Moreover, further analysis reveals that our method can effectively capture complex relationships between long-distance events, improving accordingly the performance of event relation extraction.

Document clustering is one of the most important tasks in text mining. In clustering algorithms, high-dimensional vector is usually used to represent a document which causes that the algorithms are often computationally expensive. On the other hand, Graphic Processing Unit (GPU) is increasingly important in parallel computing due to its powerful parallel capacity and high bandwidth. This paper implements a GPU-based Harmony K-means Algorithm (HKA) with NVIDIA's Compute Unified Device Architecture (CUDA), and uses it for document clustering. In our experiment, our GPU-based program can acquire a maximum 20 times speedup in contrast with CPU-based program.

ADPG: Biomedical entity recognition based on Automatic Dependency Parsing Graph

Phrase2Vec: Phrase embedding based on parsing