A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering

Sarrouti, Mourad; Alaoui, Said Ouatik El

doi:10.1016/j.jbi.2017.03.001

Cited by 57 publications

(35 citation statements)

References 28 publications

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“…(1, ) ( ) = ( (1, )̂(0 ) + (1, ) ) The above equation is in matrix form (1) ≝ (1) ( ) = ( (1)̂( 0) + (1) ), where ̂( 0) = : + 1 −1 denotes the 1 words of concatenated vector which is obtained by the sliding windows of convolution. When the convolution is applied to the deeper layer, it is described as…”

Section: Semantic Matching Patternmentioning

confidence: 99%

“…In recent years, the task of question answering plays a major role of information retrieval in human computer interaction. The required information is described in the form of questions or statements [1]. Question Answering systems presenting an interface, where users could state their demand for information in the Natural Language format and the search engine will produce suitable answers to these questions.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional recurrent neural network with template based representation for complex question answering

Reddy¹,

Madhavi²

2020

IJECE

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Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

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Section: Semantic Matching Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional recurrent neural network with template based representation for complex question answering

Reddy¹,

Madhavi²

2020

IJECE

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show abstract

“…To formulate and generate the ideal answers for a given yes/no, factoid, list or summary question, we have used the proposed retrieval model presented in (Sarrouti and Alaoui, 2017b). More specifically, after retrieving the N relevant snippets from benchmark datasets to a given biomedical question, we have re-ranked them based on the BM25 model as retrieval model, stemmed words and UMLS concepts as features.…”

Section: Ideal Answersmentioning

confidence: 99%

“…Finding accurate answers to biomedical questions written in natural language from the biomedical literature is the key to creating high-quality systematic reviews that support the practice of evidence-based medicine (Kropf et al, 2017;Wang et al, 2017;Sarrouti and Lachkar, 2017) and improve the quality of patient care (Sarrouti and Alaoui, 2017b). However, with the large and increasing volume of textual data in the biomedical domain makes it difficult to absorb all relevant information (Sarrouti and Alaoui, 2017a).…”

Section: Introductionmentioning

confidence: 99%

BioNLP 2017

Cohen¹,

Demner‐Fushman²,

Ananiadou³

et al. 2017

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According to the Association for Computational Linguistics guidelines on special interest groups (SIGs), The function of a SIG is to encourage interest and activity in specific areas within the ACL's field [1]. Is the SIGBioMed special interest group "within the ACL's field"? The titles of this year's papers suggest that it is, in that the current interest in deep learning in its many and varied manifestations is mirrored in those titles. Do those papers cover a specific area? They do, and in doing so, they demonstrate one of the great satisfactions of working in biomedical natural language processing.One of the joys of involvement in the biomedical natural language processing community is seeing the development of research with clinical applications. As examples of such work being presented at BioNLP 2017, we would like to point out the two papers that discuss the application of natural language processing to the diagnosis of neurological disorders. Bhatia et al. [2] describe an approach to using speech processing in the assessment of patients with amyotrophic lateral sclerosis (also known as Lou Gehrig's disease), one of the more horrific motor neuron diseases. Good assessment of amyotrophic lateral sclerosis patients is important for a number of reasons, including the fact that accurate tracking of the inevitable deterioration that is a hallmark of this disease gives patients and their families the possibility of purposeful planning for the attendant disability and death. However, current methodologies for evaluating the status of amyotrophic lateral sclerosis patients necessarily involve expensive equipment and highly trained personnel; when further developed, this methodology could make such evaluation much more, and more frequently, available to ALS patients. The fact that the work reported here involves a speech modality is especially exciting, as speech-related indicators of future ALS can be present long before diagnosis. The paper uses measurements of phonological features of speech and their divergence from a baseline, and demonstrates correlation with physiological measures.Adams et al. [3] describe work on detecting and categorizing word production errors associated with anomia, a particular kind of inability to find words. Screening for anomia is important because anomia is a symptom of stroke, but it is difficult and time-consuming to do, and therefore is not done as often as it should be. Automatic detection of anomia could be a nice enabler of improved care for stroke victims, but it is made difficult due to the subtlety of the phonological and semantic judgments that have to be made when assessing the phenomenon. The paper uses a combination of language modeling and phonologically-based edit distance calculation to approach the task, applying these techniques to data from the AphasiaBank collection of transcribed aphasic and healthy speech.Although we have summarized only these two examples that address neurological disorders, there are several other papers on the use of natural language proc...

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“…Hence it is not possible for searching CQA achieves for obtaining web queries [18]. The similarity between question and matching words provide the extraction features for top ranked answer [19].…”

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confidence: 99%

Hierarchy based firefly optimized K-means clustering for complex question answering

Reddy

Madhavi

2020

IJEECS

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Complex Question Answering (CQA) is commonly used for answering community questions which requires human knowledge for answering them. It is essential to find complex question answering system for avoiding the complexities behind the question answering system. In the present work, we proposed Hierarchy based Firefly Optimized k-means Clustering (HFO-KC) method for complex question answering. Initially, the given input query is preprocessed. It eliminates the way of misclassification when comparing the strings. In order to enhance the answer selection process, the obtained keywords are mapped into the candidate solution. After mapping, the obtained keywords are segmented. Each segmentation forms a new query for answer selection and various number of answers selected for each query. Okapi-25 similarity computation is utilized for the process of document retrieval. Then the answers selected are classified with K means clustering which forms the hierarchy for each answer. Finally the firefly optimization algorithm is used for selecting the best quality of answer from the hierarchy.

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A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering

Abstract: We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision.

Cited by 57 publications

References 28 publications

Convolutional recurrent neural network with template based representation for complex question answering

Convolutional recurrent neural network with template based representation for complex question answering

BioNLP 2017

Hierarchy based firefly optimized K-means clustering for complex question answering

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