"Similar query was answered earlier": processing of patient authored text for retrieving relevant contents from health discussion forum.

Online remedy finders and health-related discussion forums have become increasingly popular in recent years. Common web users write their health problems there and request suggestion from experts or other users. As a result, these forums became a huge repository of information and discussions on various health issues. An intelligent information retrieval system can help to utilize this repository in various applications. In this paper, we propose a system for the automatic identification of existing similar forum posts given a new post. The system is based on computing similarity between two patient authored texts. For computing the similarity between the current post and existing posts, the system uses a hybrid strategy based on template information, topic modelling, and latent semantic indexing. The system is tested using a set of real questions collected from a homeopathy forum namely abchomeopathy.com. The relevance of the posts retrieved by the system is evaluated by human experts. The evaluation results demonstrate that the precision of the system is 88.87%.

A study on use of the web for automatic answering of remedy finding questions of common users.

BACKGROUND: The World Wide Web has become a huge repository of knowledge in many domains, including health problems and remedy. An intelligent system, having the capability of mining the relevant information from the web, can provide instant guidance in our basic health problems. OBJECTIVE: The first objective is to convert the free-form long user query into a structured summary. The second objective is to provide an advice for a health query posed by a user. The suggestion can be in the form of names of medicines and related information or a warning to indicate that the situation is a medical emergency. METHODS: First, a set of template information is extracted from the user question. A search query is formed to retrieve relevant pages from a set of trusted websites. The retrieved pages are processed in various levels to extract the remedy and related information. RESULTS AND CONCLUSION: The system is tested using a set of real questions collected from various relevant websites. The system generated suggestions are evaluated by experts. Evaluation results show that the system provides relevant results in 92.92% cases.

A kernel-based approach for biomedical named entity recognition.

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

Feature selection techniques for maximum entropy based biomedical named entity recognition.

Named entity recognition is an extremely important and fundamental task of biomedical text mining. Biomedical named entities include mentions of proteins, genes, DNA, RNA, etc which often have complex structures, but it is challenging to identify and classify such entities. Machine learning methods like CRF, MEMM and SVM have been widely used for learning to recognize such entities from an annotated corpus. The identification of appropriate feature templates and the selection of the important feature values play a very important role in the success of these methods. In this paper, we provide a study on word clustering and selection based feature reduction approaches for named entity recognition using a maximum entropy classifier. The identification and selection of features are largely done automatically without using domain knowledge. The performance of the system is found to be superior to existing systems which do not use domain knowledge.