In this paper we have described the two basic textmining techniques namely information retrieval and information extraction. During this study, the concept of both these techniques has been introduced and presented on the basis of characteristic. We have also highlighted some application and challenges but need to be more detailed focused on different areas in future use. There are many prospective research area in this filed to give better performance and accuracy in retrieving or extracting the valuable information from various resources. Combing a domain knowledge base with textmining engine would improve its efficiency, especially in the information retrieval and information extraction.
study at a reasonable size. Investigation reports on an accident pose many challenges. Reports are written in natural language without a standard template. Spelling errors and abbreviations are often found. Composite word detection such as "safety culture", "state of mind", etc. is difficult because the order of importance is unknown. The contextual meaning of the words "security" and "culture" differs considerably, but the word "safety culture" has a completely different meaning. Therefore, context and semantics play an important role in textmining. To date, they have not reported large-scale story analysis for information that can inform security policy and design. They focused on recovery, not prediction.
Textmining is the study and practice of extracting information from text using the principles of computational linguistics. Let me introduce you a very simple data structure in textmining called feature vector, or weighted list of words. It will list the most important words in a text along with a measure of their relative importance. To do this, textmining systems perform several operations. First, commonly used words (e.g., the, and, other) are removed. Second, words are replaced by their roots. For example, eaten and eating are mapped to eat. This provides the means to measure how often a particular concept appears in a text without having to worry about minor variations.
This paper has presented an OTMM for grouping of research proposals. Research ontology is constructed to categorize the concept terms in different discipline areas and to form relationships among them. It facilitates text-mining and optimization techniques to cluster research proposals based on their similarities and then to balance them according to the applicants’ characteristics. The proposed method can be used to expedite and improve the proposal grouping process in the funding agencies and elsewhere. Currently our approach outperform well enough but at some Extent we have kept it to
Different text-mining approaches can be taken to extract chemical named entities from text. The various approaches have been categorized as dictionary-based, morphology-based (or grammar- based), and context-based . In dictionary-based approaches, different matching methods can be used to detect matches of the dictionary terms in the text . This requires good-quality dictionaries. The dictionaries are usually produced from well-known chemical databases. This approach may well capture non-systematic chemical identifiers, such as brand or generic drug names, which are source dependent and are generated at the point of registration. The drawback of a dictionary approach is that it is nearly impossible to also include all systematic chemical identifiers, such as IUPAC names  or SMILES , which are algorithmically generated based on the structure of the chemical compound and follow a specific grammar . These predefined grammars are sets of rules or guidelines developed to refer to a compound with a unique textual representation (systematic term or identifier). These terms should have a one-to-one correspondence with the structure of the compound. Grammar-based approaches expand their extractions through the capture of systematic terms by utilizing these sets of rules, for example by means of finite state machines . Therefore grammar-based approaches can extract systematic terms that are missing from the dictionaries. Both dictionary-based and grammar- based approaches may suffer from tokenization problems . Following the third approach, context-aware systems use machine learning techniques and natural language processing (NLP) to capture chemical entities. Machine learning techniques utilize the manually annotated chemical terms in a training set of documents to automatically learn and define patterns to extract terms from text . The drawback of machine learning approaches is the need for a sufficiently large annotated corpus for training the system.
Textmining for biomedicine requires a sig- nificant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of textmining appli- cations often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documen- tation of the qualities of the ontologies. A number of methodologies for evaluating on- tologies have been developed, but it is diffi- cult for users by using these methods to se- lect an ontology. In this paper, we propose a framework for selecting the most appropri- ate ontology for a particular textmining appli- cation. The framework comprises three com- ponents, each of which considers different as- pects of requirements of textmining applica- tions on ontologies. We also present an ex- periment based on the framework choosing an ontology for a gene normalization system.
Textmining has become an important research area. It deals with machine supported analysis of text. The unstructured texts which contains massive amount of information cannot simply be used for further processing by the computer and knowledge from unstructured text completed by using textmining. It uses the techniques from information retrieval, information extraction as well as natural language processing and connects them with the algorithms and methods of KDD, data mining, machine learning and statistics. In this paper we have discussed briefly about the textmining process and the techniques used in the textmining.
Text Data Mining or Knowledge-Discovery in Text (KDT) technique refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text. Textmining technique is a deviation on a countryside called data mining that tries to find interesting patterns from large databases; textmining also known as the Intelligent Text Analysis (ITA). Textmining is a young interdisciplinary field which draws on information retrieval, data mining, machine learning, statistics and computational linguistics. TextMining Technique (TMT) is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Textmining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high- quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Textmining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in textmining usually refers to some combination of relevance, novelty, and interestingness. In this paper, we introduce the rising of TextMining Technique as unforeseen-part of the Data Mining and Data Warehouse Methodologies; for improving its role, performances and productivities and also used in different research areas.
Data mining is a technique which can be used for extracting the hidden knowledge from the huge database. The data mining can be classified into various domains named as textmining, image mining, sequential pattern mining, and web mining and so. Now, we are going to discuss about the textmining, how the information can be extracted from the database of textmining. The textmining has various fields like information retrieval, document similarity, information extraction, clustering, classification and so. Searching the similar document has an important role in textmining and document management. Classification is one of the main tasks in document similarity. It is used to classify the documents based on their category. Textmining also referred as text data mining which is similar to data analytics. Textmining is the process of deriving the highly valuable information from the text. Textmining can involve the process of structuring the input text, deriving the patterns within the structure of the data, and finally
Medical textmining has gained increasing interest in re- cent years. Radiology reports contain rich information de- scribing radiologist’s observations on the patient’s medical conditions in the associated medical images. However, as most reports are in free text format, the valuable informa- tion contained in those reports cannot be easily accessed and used, unless proper textmining has been applied. In this paper, we propose a textmining system to extract and use the information in radiology reports. The system con- sists of three main modules: a medical finding extractor, a report and image retriever, and a text-assisted image fea- ture extractor. In evaluation, the overall precision and re- call for medical finding extraction are 95.5% and 87.9% respectively, and for all modifiers of the medical findings 88.2% and 82.8% respectively. The overall result of report and image retrieval module and text-assisted image feature extraction module is satisfactory to radiologists.
Textmining has become a popular research area for discovering knowledge from unstructured text data. A fundamental process and one of the most important steps in textmining is representation of text data into feature vector. Majority of textmining methods adopt a keyword-based approach to construct text representation which consists of single words or phrases. These representation models such as vector space model, do not take into account semantic information since they assume all words are independent. The performance of textmining tasks, for instance Information Retrieval (IR), Information Extraction (IE) and text clustering, can be improved when the input text data is enhanced with semantic information.
characteristics to predict the costs of extreme accidents. In conducting this assessment, the study also considers the usefulness of modern comprehensive approaches that integrate these features of text to predict accident costs. Finally, the study leaves aside the characteristics of textmining, whose importance is confirmed by predictive accuracy, for its understanding of taxpayers to rail accidents. The purpose of this final analysis is to understand railway safety information that textmining can provide, excluding fixed field ratios. These studies have shown interesting results, however, they are not able to adequately analyze the cognitive aspects of the causes of accidents. They often choose to omit important qualitative and textual information from datasets because it is difficult to create meaningful observations. The consequence of textual ignorance results in a limited analysis leading to less substantial conclusions. Textmining methods attempt to fill this void. TextMining is the discovery of new unknown information, which is automatically extracted from different written resources (text). Textmining methods can extract important concepts and emerging themes from the collection of text fonts. Used in a practical situation, the possibilities of discovering knowledge through the use of text
In this chapter we present techniques from the field of textmining to use in experiments in the work of RQ1 and RQ2. Text classification is a sub- field of textmining, and is defined as the activity of assigning predefined classes to new documents based on the likelihood suggested by a training dataset of preclassified instances (Sebastiani, 2002). The classifier may ei- ther be evaluated against an own test dataset, or other techniques may be applied. Text classification has gained popularity in recent years due to the increase in availability of digital text and better computer hardware capable of performing classification (Sebastiani, 2005, 2002; Yang and Liu, 1999). Knowledge engineering, the task of manually defining a set of rules encoding expert knowledge on how to classify documents under given categories, was until the late 1980’s the most popular approach to text classification. In recent years, however, the machine learning approach has gained popular- ity. The machine learning approach of text classification is the process of automatically building an automatic text classifier by learning from a set of previously classified documents. The latter approach has several advan- tages. First, the accuracy achieved is often comparable to that achieved by human experts. Second, since no expertise from neither domain experts nor knowledge engineers is needed to carry out the task, the machine learning approach of text classification contributes considerable savings in terms of expert manpower (Sebastiani, 2002). This project seeks to deal with this latter approach, automatically trying to create a classifier for text classifi- cation. Sebastiani (2005) defines text classification formally as:
Textmining is also called text data mining and it is defined as finding previously unknown and potentially useful from textual data, textual data may be either semi structured or unstructured. Textmining is used to extract interesting information or knowledge or pattern from the unstructured texts that are from different sources. It converts the words and phrases in unstructured information into numerical values which may be linked with structured information in database and analyzed with ancient data mining techniques. There are many techniques used in textmining such as information extraction, information retrieval, natural language processing (NLP), query processing, and categorization and clustering.
Commercial textmining products (Davi et al. 2005) are typically built in monolithic structures regarding extensibility. This is inherent as their source code is normally not available. Also, quite often interfaces are not disclosed and open standards hardly supported. The result is that the set of predefined operations is limited, and it is hard (or expensive) to write plug-ins. Therefore we decided to tackle this problem by implementing a framework for accessing text data structures in R. We concentrated on a middle ware consisting of several textmining classes that provide access to various texts. On top of this basic layer we have a virtual application layer, where methods operate without explicitly knowing the details of internal text data structures. The textmining classes are written as abstract and generic as possible, so it is easy to add new methods on the application layer level. The framework uses the S4 (Chambers 1998) class system to capture an object oriented design. This design seems best capable of encapsulating several classes with internal data structures and offers typed methods to the application layer.
Medical diagnosis is considered as an important yet complicated task that needs to be executed accurately and efficiently. The automation of this system will be very useful for the medical field. Due to recent technology advances, large masses of medical data are available. These large data contain valuable information for diagnosing diseases. Textmining techniques are using to extract useful patterns from these mass data. It provides a user- oriented approach to the novel and hidden patterns in the data. This paper intends to provide the survey of various medical textmining techniques used in medical field. The purpose of this survey is to obtain a most suitable textmining technique for the medical data.
In 2015 Yuefeng Li,et al., . discussed about the problem of existing textmining and text classification techniques. All are adopted term-based approaches. They analyze that the previous techniques suffered from the problems of polysemy and synonymy. Also they demonstrate that effective tools are required to effectively use large scale patterns. They have proposed relevance feature discovery (RFD) to find relevance features present in the text documents. They addressed two challenging issues in textmining such as, low-level support and pattern mining. Continued with RFD model they have implemented WFeatures and FClustering algorithms. FClustering algorithm describes the feature clustering process and discovers the set of patterns whereas; WFeature algorithm is used for computations of weight of classified terms.
concluded that they were more critical. All these studies have a comparable approach and in either the participants or the researchers assessed the memories. This approach might be a reason for the different findings. Therefore a more objective way could lead to a more precise outcome, which could approve to be more stable over multiple studies. The necessary objectivity might be achievable by using a textmining program. Many studies also came up with a different age of onset, but overall the conclusion was, first memories were made during the third and fourth year (Draaisma, 2005, Howes et al., 1993, Jack & Hayne, 2007, Mullen, 1994, Peterson, Grant & Boland, 2005, Tustin & Hayne, 2010).
the use of grammar induction to elucidate semantic content for textmining purposes shows promise. The H-groups shown in Table 1 provide richer semantic descriptions of the domain than keywords do, and we noted potential applications for high-level summarization of a whole corpus, the creation of information extraction templates and finer-grained text classification and retrieval. Importantly, the technique for generating H- groups would not require adaptation for use on a different corpus. The analysis in 4.2.3 suggests that the modifications that we made to the ADIOS learning regime had a beneficial effect.
A. Y. Ng,D. M. Blei, and M. I. Jordan, in  has displayed a topic modeling strategy which is a standout among the most liked probabilistic text modeling strategies and has been immediately acknowledged by machine learning and textmining . It consequently classifies documents in a collection by various topics and speaks each document with numerous topics and their respective distribution .It has ambiguity.