About Text mining

 

 

　　 What Text Mining Can Do

　　 Text mining offers a solution to this problem by replacing or supplementing the human reader with automatic systems undeterred by the text explosion. It involves analysing a large collection of documents to discover previously unknown information. The information might be relationships or patterns that are buried in the document collection and which would otherwise be extremely difficult, if not impossible, to discover. Text mining can be used to analyse natural language documents about any subject, although much of the interest at present is coming from the biological sciences.

　　 Take interactions between proteins, for example. This area of research is important for the development of drugs to modify protein interactions that are linked to disease. Text mining can not only extract information on protein interactions from documents, but it can also go one step further to discover patterns in the extracted interactions. Information may be discovered that would have been extremely difficult to find, even if it had been possible to read all the documents. This information could help to answer existing research questions or suggest new avenues to explore.

　　 How Text Mining Works

　　 Text mining involves the application of techniques from areas such as information retrieval, natural language processing, information extraction and data mining . These various stages of a text-mining process can be combined into a single workflow. We will now look in more detail at each of these areas and how, together, they form a text-mining pipeline.

　　 Information retrieval (IR) systems identify the documents in a collection which match a user ’ s query. The most well known IR systems are search engines such as Google?, which identify those documents on the WWW that are relevant to a set of given words. IR systems are often used in libraries, where the documents are typically not the books themselves but digital records containing information about the books. This is however changing with the advent of digital libraries, where the documents being retrieved are digital versions of books and journals.

　　 IR systems allow us to narrow down the set of documents that are relevant to a particular problem. As text mining involves applying very computationally intensive algorithms to large document collections, IR can speed up the analysis considerably by reducing the number of documents for analysis. For example, if we are interested in mining information only about protein interactions, we might restrict our analysis to documents that contain the name of a protein, or some form of the verb ‘to interact’ or one of its synonyms.

　　 Natural language processing (NLP) is one of the oldest and most difficult problems in the field of artificial intelligence. It is the analysis of human language so that computers can understand natural languages as humans do . Although this goal is still some way off, NLP can perform some types of analysis with a high degree of success. For example:

　　 Part-of-speech tagging classifies words into categories such as noun, verb or adjective

　　 Word sense disambiguation identifies the meaning of a word, given its usage, from the multiple meanings that the word may have

　　 Parsing performs a grammatical analysis of a sentence. Shallow parsers identify only the main grammatical elements in a sentence, such as noun phrases and verb phrases, whereas deep parsers generate a complete representation of the grammatical structure of a sentence

　　 The role of NLP in text mining is to provide the systems in the information extraction phase (see below) with linguistic data that they need to perform their task. Often this is done by annotating documents with information such as sentence boundaries, part-of-speech tags and parsing results, which can then be read by the information extraction tools.

　　 Information extraction (IE) is the process of automatically obtaining structured data from an unstructured natural language document. Often this involves defining the general form of the information that we are interested in as one or more templates, which are then used to guide the extraction process. IE systems rely heavily on the data generated by NLP systems. Tasks that IE systems can perform include:

　　 Term analysis, which identifies the terms in a document, where a term may consist of one or more words. This is especially useful for documents that contain many complex multi-word terms, such as scientific research papers

　　 Named-entity recognition, which identifies the names in a document, such as the names of people or organizations. Some systems are also able to recognize dates and expressions of time, quantities and associated units, percentages, and so on

　　 Fact extraction, which identifies and extracts complex facts from documents. Such facts could be relationships between entities or events

　　 A very simplified example of the form of a template and how it might be filled from a sentence is shown in Figure 1. Here, the IE system must be able to identify that ‘ bind ’ is a kind of interaction, and that ‘ myosin ’ and ‘ actin ’ are the names of proteins. This kind of information might be stored in a dictionary or an ontology, which defines the terms in a particular field and their relationship to each other. The data generated during IE are normally stored in a database ready for analysis in the final stage, data mining.

　　 Data mining (DM) (often also known as knowledge discovery) is the process of identifying patterns in large sets of data. The aim is to uncover previously unknown, useful knowledge. When used in text mining, DM is applied to the facts generated by the information extraction phase. Continuing with our protein interaction example, we may have extracted a large number of protein interactions from a document collection and stored these interactions as facts in a database. By applying DM to this database, we may be able to identify patterns in the facts. This may lead to new discoveries about the types of interactions that can or cannot occur, or the relationship between types of interactions and particular diseases and so on.

　　 We put the results of our DM process into another database that can be queried by the end-user via a suitable graphical interface. The data generated by such queries can also be represented visually, for example, as a network of protein interactions.