This document summarizes an algorithm to detect algorithm names in computer science research papers. It involves converting PDFs to text, performing named entity recognition to extract noun phrases, filtering entities to remove author names and locations, and using a word2vec model trained on computer science papers to classify extracted tokens as true algorithm names or noisy data by comparing their similarity to known positives and negatives. The top similar words are used to label each token as a true or false positive for an algorithm name.
Text analytics in Python and R with examples from Tobacco ControlBen Healey
Ben has been doing data sciencey work since 1999 for organisations in the banking, retailing, health and education industries. He is currently on contracts with Pharmac and Aspire2025 (a Tobacco Control research collaboration) where, happily, he gets to use his data-wrangling powers for good.
This presentation focuses on analysing text, with Tobacco Control as the context. Examples include monitoring mentions of NZ's smokefree goal by politicians and examining media uptake of BATNZ's Agree/Disagree PR campaign. It covers common obstacles during data extraction, cleaning and analysis, along with the key Python and R packages you can use to help clear them.
Text analytics in Python and R with examples from Tobacco ControlBen Healey
Ben has been doing data sciencey work since 1999 for organisations in the banking, retailing, health and education industries. He is currently on contracts with Pharmac and Aspire2025 (a Tobacco Control research collaboration) where, happily, he gets to use his data-wrangling powers for good.
This presentation focuses on analysing text, with Tobacco Control as the context. Examples include monitoring mentions of NZ's smokefree goal by politicians and examining media uptake of BATNZ's Agree/Disagree PR campaign. It covers common obstacles during data extraction, cleaning and analysis, along with the key Python and R packages you can use to help clear them.
This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a "multiple field" view of documents—we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.
An Efficient Search Engine for Searching Desired FileIDES Editor
With ever increasing data in form of e-files, there
always has been a need of a good application to search for
information in those files efficiently. This paper extends the
implementation of our previous algorithm in the form of a
windows application. The algorithm has the search timecomplexity
of Θ(n) with no pre-processing time and thus is
very efficient in searching sentences in a pool of files.
A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use different vocabulary, or the document may contain query words without being relevant. We investigate neural word embeddings as a source of evidence in document ranking. We train a word2vec embedding model on a large unlabelled query corpus, but in contrast to how the model is commonly used, we retain both the input and the output projections, allowing us to leverage both the embedding spaces to derive richer distributional relationships. During ranking we map the query words into the input space and the document words into the output space, and compute a query-document relevance score by aggregating the cosine similarities across all the query-document word pairs.
We postulate that the proposed Dual Embedding Space Model (DESM) captures evidence on whether a document is about a query term in addition to what is modelled by traditional term-frequency based approaches. Our experiments show that the DESM can re-rank top documents returned by a commercial Web search engine, like Bing, better than a term-matching based signal like TF-IDF. However, when ranking a larger set of candidate documents, we find the embeddings-based approach is prone to false positives, retrieving documents that are only loosely related to the query. We demonstrate that this problem can be solved effectively by ranking based on a linear mixture of the DESM and the word counting features.
This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a "multiple field" view of documents—we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.
An Efficient Search Engine for Searching Desired FileIDES Editor
With ever increasing data in form of e-files, there
always has been a need of a good application to search for
information in those files efficiently. This paper extends the
implementation of our previous algorithm in the form of a
windows application. The algorithm has the search timecomplexity
of Θ(n) with no pre-processing time and thus is
very efficient in searching sentences in a pool of files.
A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use different vocabulary, or the document may contain query words without being relevant. We investigate neural word embeddings as a source of evidence in document ranking. We train a word2vec embedding model on a large unlabelled query corpus, but in contrast to how the model is commonly used, we retain both the input and the output projections, allowing us to leverage both the embedding spaces to derive richer distributional relationships. During ranking we map the query words into the input space and the document words into the output space, and compute a query-document relevance score by aggregating the cosine similarities across all the query-document word pairs.
We postulate that the proposed Dual Embedding Space Model (DESM) captures evidence on whether a document is about a query term in addition to what is modelled by traditional term-frequency based approaches. Our experiments show that the DESM can re-rank top documents returned by a commercial Web search engine, like Bing, better than a term-matching based signal like TF-IDF. However, when ranking a larger set of candidate documents, we find the embeddings-based approach is prone to false positives, retrieving documents that are only loosely related to the query. We demonstrate that this problem can be solved effectively by ranking based on a linear mixture of the DESM and the word counting features.
We describe a language-independent approach to sentiment analysis (positive or negative emotions) in tweets. We also present our evaluation dataset of human-annotated sentiments in tweets, collected using Amazon Mechanical Turk.
This is the presentation I held at KDML, LWA 2012, Dortmund, Germany.
Visit http://irml.dai-labor.de/ for more information.
Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics' feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet ``I love iPhone, but I hate iPad'' can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets.
[EN] DLM Forum Industry Whitepaper 01 Capture Indexing & Auto-Classification | SER | Christa Holzenkamp | Hamburg 2002
1. Introduction
2. The importance of safe indexing
2.1 Description of the problem
2.2 The challenge of rapidly growing document volumes
2.3 The quality of indexing defines the quality of retrieval
2.4 The role of metadata for indexing and information exchange
2.5 The need for quality standards, costs and legal aspects
3. Methods for indexing and auto-categorization
3.1 Types of indexing and categorization methods
3.2 Auto-categorization methods
3.3 Extraction methods
3.4 Handling different types of information and document representations
4. The Role of Databases
4.1 Database types and related indexing
4.2 Indexing and Search methods
4.3 Indexing and retrieval methods using natural languages
5. Standards for Indexing
5.1 Relevant standards for indexing and ordering methods
5.2 Relevant standardisation bodies and initiatives
6. Best Practice Applications
6.1 Automated distribution of incoming documents Project of the Statistical Office of the Free State of Saxony
6.2 Knowledge-Enabled Content Management Project of CHIP Online International GmbH
7. Outlook
7.1 Citizen Portals
7.2 Natural language based portals
Glossary
Abbreviations
Authoring Company
Talk based on: Ricardo Baeza-Yates and Carlos Castillo: “Web Retrieval and Mining”.Entry in “Encyclopedia of Library and Information Sciences”, third edition (to appear in 2009).
ATI Courses Professional Development Short Course Remote Sensing Information ...Jim Jenkins
This three-day workshop will review remote sensing concepts and vocabulary including resolution, sensing platforms, electromagnetic spectrum and energy flow profile. The workshop will provide an overview of the current and near-term status of operational platforms and sensor systems. The focus will be on methods to extract information from these data sources. The spaceborne systems include the following; 1) high spatial resolution (< 5m) systems, 2) medium spatial resolution (5-100m) multispectral, 3) low spatial resolution (>100m) multispectral, 4) radar, and 5) hyperspectral. The two directional relationships between remote sensing and GIS will be examined. Procedures for geometric registration and issues of cartographic generalization for creating GIS layers from remote sensing information will also be discussed.
Automatically finding domain specific key terms from a given set of research paper is a challenging task and research papers to a particular area of research is a concern for many people including students, professors and researchers. A domain classification of papers facilitates that search process. That is, having a list of domains in a research field, we try to find out to which domain(s) a given paper is more related. Besides, processing the whole paper to read take a long time. In this paper, using domain knowledge requires much human effort, e.g., manually composing a set of labeling a large corpus. In particular, we use the abstract and keyword in research paper as the seeing terms to identify similar terms from a domain corpus which are then filtered by checking their appearance in the research papers. Experiments show the TF –IDF measure and the classification step make this method more precisely to domains. The results show that our approach can extract the terms effectively, while being domain independent.
This hands-on R course will guide users through a variety of programming functions in the open-source statistical software program, R. Topics covered include indexing, loops, conditional branching, S3 classes, and debugging. Full workshop materials available from http://projects.iq.harvard.edu/rtc/r-prog
A Novel Approach for Keyword extraction in learning objects using text miningIJSRD
Keyword extraction, concept finding are in learning objects is very important subject in today’s eLearning environment. Keywords are subset of words that contains the useful information about the content of the document. Keyword extraction is a process that is used to get the important keywords from documents. In this proposed System Decision tree algorithm is used for feature selection process using wordnet dictionary. WordNet is a lexical database of English which is used to find similarity from the candidate words. The words having highest similarity are taken as keywords.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Text categorization is a term that has intrigued researchers for quite some time now. It is the concept
in which news articles are categorized into specific groups to cut down efforts put in manually categorizing
news articles into particular groups. A growing number of statistical classification and machine learning
technique have been applied to text categorization. This paper is based on the automatic text categorization
of news articles based on clustering using k-mean algorithm. The goal of this paper is to automatically
categorize news articles into groups. Our paper mostly concentrates on K-mean for clustering and for term
frequency we are going to use TF-IDF dictionary is applied for categorization. This is done using mahaout
as platform.
Language-agnostic data analysis workflows and reproducible researchAndrew Lowe
This was a talk that I gave at CERN at the Inter-experimental Machine Learning (IML) Working Group Meeting in April 2017 about language-agnostic (or polyglot) analysis workflows. I show how it is possible to work in multiple languages and switch between them without leaving the workflow you started. Additionally, I demonstrate how an entire workflow can be encapsulated in a markdown file that is rendered to a publishable paper with cross-references and a bibliography (and with raw LaTeX file produced as a by-product) in a simple process, making the whole analysis workflow reproducible. For experimental particle physics, ROOT is the ubiquitous data analysis tool, and has been for the last 20 years old, so I also talk about how to exchange data to and from ROOT.
GLA-01- Java- Big O and Lists Overview and Submission Requirements You.pdfNicholasflqStewartl
GLA-01: Java, Big O and Lists
Overview and Submission Requirements
Your task is to work individually to create a series of methods that can solve tech interview
questions, as well as analyze the computational complexity of these solutions. You should
complete your entire lab in a single file named InterviewQuestions.java. Once you have
completed the lab, you should submit InterviewQuestions.java to D2L.
External Resources and Code
As per the Academic Integrity guidelines, you may not copy code (even with modification) from
anywhere, including the internet, other students, or your textbook. You may not consult other
students or look at their code. You may not share your code with other students. Any submission
that violates the academic honesty guidelines will receive an automatic 0 and will be considered
an academic honesty violation.
Background: Tech Interviews
Technical interviews are a common part of the hiring process in the software development field.
Although they can range in format, one of the most common techniques is to ask candidates to
solve a couple of programming problems on a whiteboard and then explain their solutions. This
GLA takes the form of a number of small programming problems that could appear in such an
interview.
For each problem, implement the method in InterviewQuestions.java, explain what n is, and state
the Big(O) complexity of your solution.
Problem One: Pricey Neighbours
Suppose you have an array of doubles that represents the value of each house on a long block of
houses. Find the three adjacent houses that have the largest combined value, and return the
smallest index of the array (leftmost house).
Your solution should be of the form: (Note: you may use the provided template)
public int findPriceyNeighbours(double[] prices)
The method header should state what n is (Java Commented form), and what the Big(O)
complexity of your solution is.
Problem Two: Common Friends
Suppose you have two ArrayLists, each of which represents the friends of a single person. Write
a method to find the common friends between those two people-- that is, a list of Strings that
appear in both input lists.
Your solution should be of the form: (Note: you may use the provided template)
public ArrayList<String> commonFriends(ArrayList<String> friendListOne, ArrayList<String>
friendListTwo)
The method header should state what n is (Java Commented form), and what the Big(O)
complexity of your solution is.
Problem Three: Count Divisors (Note: you may use the provided template)
Suppose you have an array of integers. Count each pair of indices in that array in which the value
at the first index is evenly divisible by the values at the following indices.
Your solution should be of the form:
public int countDivisors(int[] values)
The method header should state what n is (Java Commented form), and what the Big(O)
complexity of your solution is.
Problem Four: First Odd Number
Suppose you have an array of integers. All of the integers from indexes .
This project is a classification and analysis of unstructured data and also has the power to classify the different type of data like text, jpg, pdf, doc, png, py, c, c++. java, exe and many more from a single folder.
A web browser takes you anywhere on the internet, letting you see text, images and video from anywhere in the world. ... The web is a vast and powerful tool. Over .
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Algorithm Name Detection & Extraction
1. Algorithm Name Detection
in Computer Science Research Papers
Information Retrieval & Extraction Course
IIIT HYDERABAD
Submitted To: Prof. Vasudev Verma
Submission By: Team 41
Allaparthi Sriteja [201302139]
Deeksha Singh Thakur [201505627]
Sneh gupta [201302201]
2. Aim of project
Processing the contents of the research document
List out the name of algorithms being discussed in the paper
Assist the users to find research papers specific to a domain without actually
opening and reading each of them.
Extraction of Algorithm Name from Research Paper
3. Converting pdf to text
Input : A research paper in the pdf format.
Output : Need to convert that pdf to text format.
Processing : Using PDFMiner
pdf2txt.py -O myoutput -o myoutput/myfile.text -t text myfile.pdf
Usage:
pdf2txt.py [options] filename.pdf
Options: -o output file name
-t output format (text/html/xml/tag[for Tagged PDFs])
-O dirname (triggers extraction of images from PDF into directory)
4. Named Entity Recognition
Input : Research paper in the text format.
Output : Noun phrases (NNPS and NNs)
Processing :
Sentence tokenization
Merging the divided words at the end of the line [ex: div - n ision]
Removing the part before the Abstract and after the Reference.
Find the citation sentences and extract them
Do pos_tagging for those sentences.
Now extract the NNPS and NN. combine the NNPS occurring adjacent to each other in a sentence.
5. Filtration of the Named Entities
Input : Named Entities with author names, University names, places.
Output : stemmed desired named entities using porter stemmer.
Processing:
Designed the list of authors and universities and places.
And compare the named entities with these lists and filter them.
Search for the word algorithm or technique to give more weightage to that particular word as the
probability of getting the algorithm name will be high in such sentences.
Stem these remaining named entities using Porter Stemmer
7. Input : Named Entities from Research Papers
-From each research paper in the corpus, we obtain a set of Named Entities
Eg.
-These NE’s are filtered for
author name geographical locations organization names dataset names
BUT THE DATA STILL CONTAINS NOISE!!!
neighbo
rhood
sparseli
nearme
thod
mov
i
slim
tabl matrixf
actor
hosli
m
ratin
gpre
dict
8. TASK :
Separate noisy data from names of actual algorithms
Using WORD2VEC
From Gensim library
Gensim is a FREE Python library that allows
-Making and Importing word2vec models
-Determine similarity between words in the model
-Determine topN most similar words to a given word
9. WORD2VEC MODEL :
The word2vec model under consideration contains -
word2vec word vectors
trained on ~4.3lac computer science papers, 3.7B tokens
A 300 dimensional vector representation of all 1 word algorithm names
Used as model[‘word’] = {[300 dimension vector], dtype: float}
10. Classifying the tokens :
Form a list,(manually by going through some papers) -
true positives[containing name of actual computer science algorithms]
false positives [most common noise components in each paper].
Compare each named entity extracted from paper with these lists of TPs and FPs
and find the similarity between them. If the similarity between a word and another
word in TP is greater than a threshold value (0.4 considered in our case), classify
it as the TP, otherwise FP.
![Algorithm Name Detection
in Computer Science Research Papers
Information Retrieval & Extraction Course
IIIT HYDERABAD
Submitted To: Prof. Vasudev Verma
Submission By: Team 41
Allaparthi Sriteja [201302139]
Deeksha Singh Thakur [201505627]
Sneh gupta [201302201]](https://image.slidesharecdn.com/ire1-160415133440/85/Algorithm-Name-Detection-Extraction-1-320.jpg)
