Vector spaces for information extraction - Random Projection Example
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This document discusses using vector spaces for information extraction. It explains that entities to be extracted or compared are represented as vectors in a large and dynamic space based on the contexts surrounding them. Dimension reduction techniques are needed due to the massive size of these spaces. The document proposes using random projection, which creates a random projection matrix to estimate the vector space in a way that maintains distances between vectors. An example application is described where technology terms are extracted from publications by classifying them based on words and positions in a reduced 2000 dimension space using random projection.
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This document provides an introduction to SPARQL 1.1. It begins by explaining that SPARQL is a query language for the semantic web that allows users to query RDF data stores similarly to how SQL queries relational databases. It then describes SPARQL 1.0, the initial standard version, and the new features being added in SPARQL 1.1, including aggregate functions, subqueries, property paths and federated querying. The document concludes by discussing SPARQL implementations and the status of the 1.1 specification.Ad
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M.A. Sem - 2 | Presentation
Presentation Season - 2
Paper - 108: The American Literature
Submitted Date: April 2, 2025
Paper Name: The American Literature
Topic: Thoreaus Influence on Gandhi: The Evolution of Civil Disobedience
[Please copy the link and paste it into any web browser to access the content.]
Video Link: https://youtu.be/HXeq6utg7iQ
For a more in-depth discussion of this presentation, please visit the full blog post at the following link: https://rajdeepbavaliya2.blogspot.com/2025/04/thoreau-s-influence-on-gandhi-the-evolution-of-civil-disobedience.html
Please visit this blog to explore additional presentations from this season:
Hashtags:
#CivilDisobedience #ThoreauToGandhi #NonviolentResistance #Satyagraha #Transcendentalism #SocialJustice #HistoryUncovered #GandhiLegacy #ThoreauInfluence #PeacefulProtest
Keyword Tags:
civil disobedience, Thoreau, Gandhi, Satyagraha, nonviolent protest, transcendentalism, moral resistance, Gandhi Thoreau connection, social change, political philosophyBirnagar High School Platinum Jubilee Quiz.pptx
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CURRENT CASE COUNT: 919
?Texas: 744 (+2) (55% of cases are in Gaines County).
?New Mexico: 81 (83% of cases are from Lea County).
?Oklahoma: 20 (+2)
?Kansas: 74 (+5) (38.89% of the cases are from Gray County).
HOSPITALIZATIONS: 104
? Texas: 96 (+2) C This accounts for 13% of all cases in Texas.
? New Mexico: 7 C This accounts for 9.47% of all cases in New Mexico.
? Kansas: 3 C This accounts for 5.08% of all cases in the state of Kansas.
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?Texas: 2 C This is 0.27% of all cases in Texas.
?New Mexico: 1 C This is 1.23% of all cases in New Mexico.
US NATIONAL CASE COUNT: 1,197
INTERNATIONAL SPREAD
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?Chihuahua, Mexico: 2,179 (+239) cases, 4 fatalities, 7 currently hospitalized.
?Canada: 3,207 (+208), 1 fatality
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Vector spaces for information extraction - Random Projection Example
- 2. Vector?Spaces?in?Information?Extraction Entities?to?be?Extracted?or?compared Contexts?that?are?used?for?comparison ? Vector?spaces?in?IE?are: ? a representation?framework?for?the Distributional?Hypothesis*;? ? Sparse; ? Large?(order?of?millions?by?millions); ? Changing?Dynamically; *not?exclusively?
- 3. Vector?Spaces?in?Information?Extraction Entities?to?be?Extracted?or?compared Contexts?that?are?used?for?comparison ? In?classic?methods?the dimension? of?VSM growths?as data?growth.? ? Dimension?Reduction?techniques? based?on?Matrix?Factorization? may?not?be?applied: ? Iterative?methods?are?still?of the? complexity?of?O(n2)?
- 4. Vector?Spaces?in?Information?Extraction ? Random?Projection?is?one?solution: ? Estimate?a?VSM by?a?random?projection?matrix?that made? of?a?set?of?randomly?created?vectors. ? i.e.?based on?the?Johnson\Lindenstrauss lemma ? verified by?the?results?reported?in?(Hecht\Nielsen,?1994) *?The?above?figure?is?copyrighted?by?Alex?Clemmer (http://nullspace.io/)?
- 5. Vector?Spaces?in?Information?Extraction ? Random?Projection?\ Application? Example ? Extraction?of?Technology?Terms?(term?classification) ? Data?Size:?only 10,000 publication ? Contexts: words?and?their?position in?the? neighbourhood?of?terms ? Original?Dimension:? ? approximately? 5 million ? Reducing?the?dimension?to?2000 using? Random?Projection Behrangs research?evolves?around?classification?and?finding?the?optimal?contexts?in?random?vector?spaces?for??the?extraction?of?technology?terms?and?their?relation.?If?you?are?interested?please?email?him?at? behrangatoffice@gmail.com