Similarity Measurement Preliminary Results
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This document summarizes preliminary results from measuring similarity between folksonomies and LSA. It crawled over 7.5 million tags from Delicious and processed them to create tag vectors for measuring co-occurrence counts and cosine similarity between tags. It also created an LSA corpus from over 17,000 web pages to generate a term-document matrix. The document analyzes correlations between different similarity measures and provides examples of related tags for the term "linguistics".
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Otot Manusia
Dokumen tersebut membahas tentang pengertian otot manusia. Otot manusia memungkinkan manusia untuk bergerak dan melakukan aktivitas. Otot terdiri dari tiga jenis, yaitu otot polos, otot lurik, dan otot jantung. Otot berfungsi sebagai alat gerak aktif yang dapat menggerakkan organ lain untuk menimbulkan gerakan.
A Document Similarity Measurement without Dictionaries
The document proposes a measure of document similarity called Common Keyword Similarity (CKS) that does not rely on dictionaries. CKS is based on finding common substrings between documents using a PAT-tree data structure. The importance of each substring is determined by its discriminating effect (KDE), which reflects how well it fits a given classification system. CKS is computed as the sum of the weights of the common keywords between two documents. Experimental results on news articles show that CKS without a dictionary has better recall and precision than a method using cosine coefficient that relies on a dictionary, since many terms cannot be found in dictionaries. The classification system used to determine keyword weights also significantly impacts performance.
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International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
K017367680
This document summarizes various stemming algorithms used for information retrieval. It discusses rule-based stemming algorithms like the Porter stemmer and Lovins stemmer which use language rules to remove suffixes and extract word stems. It also describes statistical stemming methods which use machine learning on text corpora to identify morphological variants. Finally, it analyzes different techniques for conflating words during stemming, such as affix removal, successor variety, table lookup, and n-gram methods.
L0261075078
This document presents an algorithm for semantic-based similarity measure (SBSM) to improve text clustering. The algorithm assigns semantic weights to documents terms and phrases based on their use as arguments in proposition bank notation. It calculates similarity between a document and query based on matching weighted terms and phrases. Experimental results on a dataset show the SBSM using proposition bank notation achieves better performance than traditional similarity measures for text clustering.
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Stop thinking, start tagging - Tag Semantics emerge from Collaborative Verbosity
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
Token
Record linkage is used to identify records from different data sources that represent the same real-world entity. It involves preprocessing data, reducing the search space using blocking methods, computing similarity functions to compare records, and applying decision models to classify record pairs. A common blocking method is the sorted neighborhood method, which sorts records by a blocking key and compares nearby records within a fixed window. The effectiveness of record linkage depends on selecting good blocking keys and similarity functions.
Extractive Document Summarization - An Unsupervised Approach
1. This paper presents and evaluates an unsupervised extractive document summarization system that uses TextRank, K-means clustering, and one-class SVM algorithms for sentence ranking.
2. The system achieves state-of-the-art performance on the DUC 2002 English dataset with a ROUGE score of 0.4797 and can also summarize Swedish documents.
3. Domain knowledge is added through sentence boosting to improve summarization of news articles, and similarities between sentences are calculated to avoid redundancy for multi-document summarization.
Chat bot using text similarity approach
1. There are three main techniques for chat bots to generate responses: static responses using templates, dynamic responses by scoring potential responses from a knowledge base, and generated responses using deep learning to generate novel responses from training data.
2. Text similarity can be measured using string-based, corpus-based, or knowledge-based approaches. String-based measures operate on character sequences while corpus-based measures use word co-occurrence statistics and knowledge-based measures use information from semantic networks like WordNet.
3. Popular corpus-based measures include LSA, ESA, and PMI-IR which analyze word contexts and co-occurrences in corpora. Knowledge-based measures like Resnik, Lin, and Leacock
International Journal of Engineering Research and Development
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Aspects of broad folksonomies
This document analyzes aspects of broad folksonomies based on a sample of over 800,000 bookmarks from Delicious. It finds that:
1) The frequency-rank distribution of co-occurring tags follows a power law for around 80% of tags analyzed.
2) When analyzing resource-based and user-based tagging characteristics, only around 18-13% of tags respectively followed a power law distribution, indicating folksonomies have a more complex underlying structure.
3) Tags provide added value for retrieval over title alone, though precision and recall were mostly below 0.5, showing tags are not redundant but current usage may not optimize retrieval performance.
Seeds Affinity Propagation Based on Text Clustering
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Textmining Retrieval And Clustering
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors and is often used to compare documents, with higher values indicating more similar documents. K-means clustering partitions documents into k groups to minimize intra-cluster similarity, while hierarchical clustering creates a dendrogram of document clusters by progressively merging the most similar pairs. The EM algorithm computes maximum likelihood estimates for document clustering when data is incomplete. Evaluation of document clusters considers internal metrics like intra-cluster similarity and inter-cluster dissimilarity.
Textmining Retrieval And Clustering
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors and is often used to compare documents, with higher values indicating more similar documents. K-means clustering partitions documents into k groups to minimize intra-cluster similarity, while hierarchical clustering creates a dendrogram of document clusters by progressively merging the most similar groups. The EM algorithm computes maximum likelihood estimates for document clustering when data is incomplete. Evaluation of document clusters considers internal metrics like intra-cluster similarity and inter-cluster dissimilarity.
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
The document presents an algorithm for optimized searching using non-overlapping iterative neighbor intervals. The algorithm aims to reduce the number of checked conditions by saving the frequency of replicated words and using non-overlapping intervals based on the plane sweep algorithm. It does this by focusing the search on a smaller subspace of relevant intervals near the minimum frequency keyword. The algorithm iterates through ranges, eliminating unsatisfied keywords to detect relevant ranges. This improves efficiency and reduces the number of comparisons compared to previous methods.
unit-4.ppt
This document discusses stemming algorithms which are used to improve information retrieval effectiveness and reduce indexing file sizes. It describes different types of stemming algorithms including table lookup, successor variety, n-gram, and affix removal approaches. The Porter stemming algorithm is also explained. Experimental evaluations have found that stemming generally has a positive impact on retrieval performance and that stemmers can significantly reduce indexing file sizes by up to 50%.
unit 4.ppt
This document discusses stemming algorithms which are used to improve information retrieval effectiveness and reduce indexing file sizes. It describes different types of stemming algorithms including table lookup, successor variety, n-gram, and affix removal approaches. The Porter stemming algorithm is also explained. Experimental evaluations have found that stemming generally has a positive impact on retrieval performance and that stemmers can significantly reduce indexing file sizes by up to 50%.
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATA
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Identifying the semantic relations on
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
This paper proposes three algorithms (ScanCount, MergeSkip, and DivideSkip) to efficiently search for approximate string matches from a collection of strings. The algorithms utilize inverted indexes of substrings (grams) to find candidate strings. The paper also studies how to integrate filtering techniques with the merging algorithms to further improve performance. Experiments show the proposed techniques significantly outperform existing algorithms and that filters need to be judiciously combined with merging to achieve the best results.
2014-mo444-practical-assignment-02-paulo_faria
The document discusses using clustering techniques like K-means, LDA, and PAM to analyze topics in a large dataset of Wikipedia documents. It explores preprocessing steps, compares different clustering algorithms, and analyzes the results. K-means identified around 250 topics using the elbow method. LDA was able to identify coherent topics based on word co-occurrence. PAM using bigrams found some meaningful word pairs but the clusters did not separate well. The techniques revealed topics related to music, politics, war and more.
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International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering and Science Invention (IJESI)
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Stop thinking, start tagging - Tag Semantics emerge from Collaborative Verbosity
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Extractive Document Summarization - An Unsupervised Approach
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International Journal of Engineering Research and Development
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Seeds Affinity Propagation Based on Text Clustering
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AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
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Similarity Measurement Preliminary Results
- 1. Similarity measurement:Folksonomyvs.LSA Preliminary Results
- 2. The Tripartite structure of tagging Folksonomy is a set of triples < user , tag, object> A folksonomy is a tuple F :=(U, T, R, Y) where U, T, and R are finite sets, whose elements are called users, tags and resources. Y is a ternary relation between user, tags and resources.
- 3. Del.icio.us Tag distribution Tag distribution Log-log Tag distribution After crawling the delicious.com site, the total number tags (tokens) obtained was 7,528,528, among which the number of types was 188,964. All the tags are stemmed using the Porter Stemmer and the total number of stemmed tags ended up to be 174,887.
- 4. LSA Processing Workflow in R tm = textmatrix(‘dir/‘) tm = lw_logtf(tm) * gw_idf(tm) space = lsa(tm, dims=dimcalc_share()) as.textmatrix(tm)
- 5. LSA corpus preparing A total number of 17,085 web pages were crawled and were later parsed to remove all the HTML markups. Stemming and Stop-word removal The processed corpus:14,993,620 tokens, 259,464 types of words. Only words with frequency more than 100 were kept to be entered into a word-by-document matrix. There were 1047 words with frequency more than 100. The text length kept in the corpus is from ranges from 51 words per document to 4009 words per document . Therefore, the resulting term-document matrix have 3465 columns (documents) and 1082 rows (words).
- 6. LSA document length distribution The text length kept in the corpus is from ranges from 51 words per document to 4009 words per document .
- 7. Three similarity measurements Tag Co-coccurrence counts Tag vector cosine similarity LSA
- 8. Similarity Measurement Tag Co-occurrence Counts: 1)simple count: how many times two tags are used by the same user to annotate the same resource 2)normalized count: Jaccard IndexThe co-occurrence counts of tag A and tag B divided by the joint frequency of A and B.
- 9. Distribution of Tag co-occurrence Counts (simple counts)
- 10. Distribution of Tag co-occurrence Counts (normalized)
- 11. Measurement 2: Cosine Similarity Based on the co-occurrence vector of each tag with every other tag. Since there are normalized and unnormalized tag co-occurrence counts, we then ended up with two X and Y are the co-occurrence vectors of two distinct tags.
- 12. Distribution of Tag Cosine Similarity
- 13. Distribution of Tag Cosine Similarity based on normalized Tag Co-occurrence counts
- 14. Results The pair-wise Pearson correlation and Spearman correlation among 5 measurements [Tag-cooccurrence count, Tag cosine similarity, LSA, normalized Tag-cooccurrence count, Tag cosine similarity based on normalized tag-tag cooccurrence matrix]
- 15. Correlation
- 21. Qualitative Insight – ‘linguistics’ Top 10 “linguistics” related words according to 5 measurements
- 22. Correlation between two measurements:normalized tag co-occurrence counts vs. normalized tag cosine P= 0.7547
Editor's Notes
- Spearman’s rank correlation