The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform

A Mekala - One of the best experts on this subject based on the ideXlab platform.

  • DATA
    2013
    Co-Authors: A Mekala
    Abstract:

    Generally the searcher either searches for exact information based on the query or just surf topics which interest them on websites. Naturally, when user enters a query related to some topics, they did not get exact result of what they want. If the system selected the relevant passages, grouping together, made it summarizing and fluently, and returned the resulting text it will be an advantage to the user. Otherwise, if the resulting summary is not relevant enough to searcher, the user can refine the query. Thus, as a result, summarization is used as a technique for improving querying. To ensure this technique they proposed to summarize the content of a temporal topic in existing work by using an anatomy based summarization method called Topic Summarization and Content Anatomy (TSCAN). A temporal similarity (TS) function is implemented to generate the event dependencies and context similarity to form an evolution graph of the topic search. In this paper, we are combining two methods for topic summarization. The first method is mainly based on Termfrequency, while the second method is based on ontology. We will construct an ontology database for analyzing the main topics of the article using NPL tool. understanding of a number of disparate fields, including linguistics, computer science, and statistics

  • an ontology based anatomy approach to temporal topic summarization
    International Journal of Computer Applications, 2012
    Co-Authors: A Mekala, Chandra C Sekar
    Abstract:

    Generally the searcher either searches for exact information based on the query or just surf topics which interest them on websites. Naturally, when user enters a query related to some topics, they did not get exact result of what they want. If the system selected the relevant passages, grouping together, made it summarizing and fluently, and returned the resulting text it will be an advantage to the user. Otherwise, if the resulting summary is not relevant enough to searcher, the user can refine the query. Thus, as a result, summarization is used as a technique for improving querying. To ensure this technique they proposed to summarize the content of a temporal topic in existing work by using an anatomy based summarization method called Topic Summarization and Content Anatomy (TSCAN). A temporal similarity (TS) function is implemented to generate the event dependencies and context similarity to form an evolution graph of the topic search. In this paper, we are combining two methods for topic summarization. The first method is mainly based on Termfrequency, while the second method is based on ontology. We will construct an ontology database for analyzing the main topics of the article using NPL tool.

Sashka T Davis - One of the best experts on this subject based on the ideXlab platform.

  • vector space and language models for scientific document summarization
    2015
    Co-Authors: John M Conroy, Sashka T Davis
    Abstract:

    In this paper we compare the performance of three approaches for estimating the latent weights of terms for scientific document summarization, given the document and a set of citing documents. The first approach is a Termfrequency (TF) vector space method utilizing a nonnegative matrix factorization (NNMF) for dimensionality reduction. The other two are language modeling approaches for predicting the term distributions of human-generated summaries. The language model we build exploits the key sections of the document and a set of citing sentences derived from auxiliary documents that cite the document of interest. The parameters of the model may be set via a minimization of the Jensen-Shannon (JS) divergence. We use the OCCAMS algorithm (Optimal Combinatorial Covering Algorithm for Multi-document Summarization) to select a set of sentences that maximizes the term-coverage score while minimizing redundancy. The results are evaluated with standard ROUGE metrics, and the performance of the resulting methods achieve ROUGE scores exceeding those of the average human summarizer.

John M Conroy - One of the best experts on this subject based on the ideXlab platform.

  • vector space and language models for scientific document summarization
    2015
    Co-Authors: John M Conroy, Sashka T Davis
    Abstract:

    In this paper we compare the performance of three approaches for estimating the latent weights of terms for scientific document summarization, given the document and a set of citing documents. The first approach is a Termfrequency (TF) vector space method utilizing a nonnegative matrix factorization (NNMF) for dimensionality reduction. The other two are language modeling approaches for predicting the term distributions of human-generated summaries. The language model we build exploits the key sections of the document and a set of citing sentences derived from auxiliary documents that cite the document of interest. The parameters of the model may be set via a minimization of the Jensen-Shannon (JS) divergence. We use the OCCAMS algorithm (Optimal Combinatorial Covering Algorithm for Multi-document Summarization) to select a set of sentences that maximizes the term-coverage score while minimizing redundancy. The results are evaluated with standard ROUGE metrics, and the performance of the resulting methods achieve ROUGE scores exceeding those of the average human summarizer.

Chandra C Sekar - One of the best experts on this subject based on the ideXlab platform.

  • an ontology based anatomy approach to temporal topic summarization
    International Journal of Computer Applications, 2012
    Co-Authors: A Mekala, Chandra C Sekar
    Abstract:

    Generally the searcher either searches for exact information based on the query or just surf topics which interest them on websites. Naturally, when user enters a query related to some topics, they did not get exact result of what they want. If the system selected the relevant passages, grouping together, made it summarizing and fluently, and returned the resulting text it will be an advantage to the user. Otherwise, if the resulting summary is not relevant enough to searcher, the user can refine the query. Thus, as a result, summarization is used as a technique for improving querying. To ensure this technique they proposed to summarize the content of a temporal topic in existing work by using an anatomy based summarization method called Topic Summarization and Content Anatomy (TSCAN). A temporal similarity (TS) function is implemented to generate the event dependencies and context similarity to form an evolution graph of the topic search. In this paper, we are combining two methods for topic summarization. The first method is mainly based on Termfrequency, while the second method is based on ontology. We will construct an ontology database for analyzing the main topics of the article using NPL tool.

Sheikha Hassan - One of the best experts on this subject based on the ideXlab platform.

  • Text mining Twitter social media for Covid-19 : Comparing latent semantic analysis and latent Dirichlet allocation
    Högskolan i Gävle Avdelningen för datavetenskap och samhällsbyggnad, 2020
    Co-Authors: Sheikha Hassan
    Abstract:

    In this thesis, the Twitter social media is data mined for information about the covid-19 outbreak during the month of March, starting from the 3’rd and ending on the 31’st. 100,000 tweets were collected from Harvard’s opensource data and recreated using Hydrate. This data is analyzed further using different Natural Language Processing (NLP) methodologies, such as Termfrequency inverse document frequency (TF-IDF), lemmatizing, tokenizing, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Furthermore, the results of the LSA and LDA algorithms is reduced dimensional data that will be clustered using clustering algorithms HDBSCAN and K-Means for later comparison. Different methodologies are used to determine the optimal parameters for the algorithms. This is all done in the python programing language, as there are libraries for supporting this research, the most important being scikit-learn. The frequent words of each cluster will then be displayed and compared with factual data regarding the outbreak to discover if there are any correlations. The factual data is collected by World Health Organization (WHO) and is then visualized in graphs in ourworldindata.org. Correlations with the results are also looked for in news articles to find any significant moments to see if that affected the top words in the clustered data. The news articles with good timelines used for correlating incidents are that of NBC News and New York Times. The results show no direct correlations with the data reported by WHO, however looking into the timelines reported by news sources some correlation can be seen with the clustered data. Also, the combination of LDA and HDBSCAN yielded the most desireable results in comparison to the other combinations of the dimnension reductions and clustering. This was much due to the use of GridSearchCV on LDA to determine the ideal parameters for the LDA models on each dataset as well as how well HDBSCAN clusters its data in comparison to K-Means