The document summarizes key topics to be covered in Lecture 01 on discrete mathematics including: propositions which are statements that are either true or false; truth tables; logical operators like conjunction, disjunction, exclusive OR, and implication; bi-conditional statements; compound propositions; precedence of logical operators; translating sentences; and Boolean searches and bit operations. Examples of propositions and non-propositions are provided.
Slides for Muslims in ML workshop presentation at NeurlPS 2020 on December 8, 2020 - this is a shorter 25 minute version of the UMass Lowell talk of November 2020 (so the slides are a subset of that).
The document discusses automatically identifying Islamophobia in social media text. It begins by introducing the speaker and their areas of research, including hate speech detection. It then provides background on Islamophobia, discussing its origins and definitions. The remainder of the document outlines a project to collect and annotate Twitter data containing mentions of Ilhan Omar to detect Islamophobic sentiment, discussing the pilot annotation process and lessons learned.
Hate speech is language intended to cause harm against a particular individual or group, often based on their racial, ethnic, religious, or gender identity. Hate speech is widespread on social media, and is increasingly common in mainstream political discourse. That said, there is no clear consensus as to what constitutes hate speech. In addition, human moderators come with their own biases, and automatic computer algorithms are often easy to fool. All of these factors complicate the efforts of social media platforms to filter or reduce such content. During this interactive workshop we will discuss examples from Twitter in the hopes of reaching some consensus as to what is and is not hate speech. We will also try to determine what kind of knowledge a human moderator or an automatic algorithm would need to have in order to make this determination. We will try to avoid particularly graphic examples of hate speech and focus on more subtle cases.
Talk on Algorithmic Bias given at York University (Canada) on March 11, 2019. This is a shorter version of an interactive workshop presented at University of Minnesota, Duluth in Feb 2019.
The document summarizes key topics to be covered in Lecture 01 on discrete mathematics including: propositions which are statements that are either true or false; truth tables; logical operators like conjunction, disjunction, exclusive OR, and implication; bi-conditional statements; compound propositions; precedence of logical operators; translating sentences; and Boolean searches and bit operations. Examples of propositions and non-propositions are provided.
Slides for Muslims in ML workshop presentation at NeurlPS 2020 on December 8, 2020 - this is a shorter 25 minute version of the UMass Lowell talk of November 2020 (so the slides are a subset of that).
The document discusses automatically identifying Islamophobia in social media text. It begins by introducing the speaker and their areas of research, including hate speech detection. It then provides background on Islamophobia, discussing its origins and definitions. The remainder of the document outlines a project to collect and annotate Twitter data containing mentions of Ilhan Omar to detect Islamophobic sentiment, discussing the pilot annotation process and lessons learned.
Hate speech is language intended to cause harm against a particular individual or group, often based on their racial, ethnic, religious, or gender identity. Hate speech is widespread on social media, and is increasingly common in mainstream political discourse. That said, there is no clear consensus as to what constitutes hate speech. In addition, human moderators come with their own biases, and automatic computer algorithms are often easy to fool. All of these factors complicate the efforts of social media platforms to filter or reduce such content. During this interactive workshop we will discuss examples from Twitter in the hopes of reaching some consensus as to what is and is not hate speech. We will also try to determine what kind of knowledge a human moderator or an automatic algorithm would need to have in order to make this determination. We will try to avoid particularly graphic examples of hate speech and focus on more subtle cases.
Talk on Algorithmic Bias given at York University (Canada) on March 11, 2019. This is a shorter version of an interactive workshop presented at University of Minnesota, Duluth in Feb 2019.
The document discusses the history and evolution of dictionaries from the first English dictionary in 1604 to modern computational approaches using natural language processing. It describes early dictionaries like Robert Cawdrey's Table Alphabeticall and Samuel Johnson's A Dictionary of the English Language. Later influential dictionaries included Noah Webster's American Dictionary of the English Language and the Oxford English Dictionary. The document proposes that natural language processing techniques like analyzing word frequencies, collocations, and measures of association could help identify emerging words and senses in new text, similar to the work of lexicographers in compiling dictionaries.
The document summarizes research on using lexical decision lists to screen Twitter users for depression and PTSD. It finds that a simple machine learning method using n-grams of varying length up to 6 words and binary weighting achieved the best results. Emoticons and emojis were strong indicators. The top features indicating depression included terms expressing sadness, while PTSD indicators included abbreviations and URLs. It suggests self-reporting of conditions may indicate something else requiring discussion.
Poster presented at the Semeval 2015 workshop. Our system clustered words based on their contexts in order to identify their underlying meanings or senses.
This document provides an overview of what it would be like to complete a Master's thesis under Dr. Ted Pedersen. It discusses that research involves asking interesting questions about the world and conducting experiments to answer those questions. Dr. Pedersen's research interests include natural language processing tasks like word sense disambiguation, semantic similarity, and collocation discovery. To succeed, a student needs enthusiasm for research, strong writing skills, and the ability to work independently while communicating regularly with Dr. Pedersen. Previous students have explored various NLP topics and many have gone on to PhD programs. The reading provided is intended to assess the student's understanding and interest in Dr. Pedersen's research areas.
This document summarizes a tutorial on measuring the similarity and relatedness of concepts. It discusses the distinction between semantic similarity and relatedness. It describes several common measures of similarity that use information from ontologies, such as path-based measures, measures that incorporate path and depth, and measures that incorporate information content. It also discusses measures of relatedness that can be used for concepts that are not connected by ontological relations, such as definition-based measures and measures based on gloss vectors constructed from corpus data. Experimental results generally show that gloss vector measures perform best, followed by definition-based measures, with path-based measures performing the worst.
Some thoughts on what it's like to do a Master's thesis with me, including general ideas about research, my research interests, and a few suggestions as to what will lead to success
This document describes UMLS::Similarity, an open source software that measures the semantic similarity or relatedness of biomedical terms from the Unified Medical Language Systems (UMLS). It provides several measures to quantify similarity/relatedness based on the hierarchical structure and definitions of terms in the UMLS. The software can be used via command line, API, or web interface and has been used in applications like word sense disambiguation.
The document discusses word sense induction systems developed at the University of Minnesota Duluth that were used to cluster web search results. The systems represented web snippets using second-order co-occurrences and were evaluated in Task 11 of SemEval-2013. The best performing system (Sys1) used more data in the form of web-like text and achieved an F-10 score of 46.53, outperforming systems that used larger amounts of out-of-domain news text. Future work could look at augmenting data by expanding snippets and using more web-based resources like Wikipedia.
These are the slides for a talk given at the University of Alabama, Birmingham on April 19, 2013. The title of the talk is "Measuring Similarity and Relatedness in the Biomedical Domain : Methods and Applications"
Measuring Semantic Similarity and Relatedness in the Biomedical Domain : Methods and Applications - presented Feb 21, 2012 as a webinar to the Mayo Clinic BMI group.
The document summarizes a tutorial on measuring semantic similarity and relatedness between medical concepts. It introduces different types of measures, including path-based measures, measures using information content that incorporate concept specificity, and measures of relatedness that use definition overlaps or corpus co-occurrence information. The tutorial aims to explain the distinction between similarity and relatedness, describe available measures, and how to evaluate and apply them in clinical natural language processing tasks.
The document describes experiments conducted to evaluate measures of association for identifying the compositionality of word pairs. It discusses two hypotheses: 1) word pairs with higher association scores are less compositional, and 2) more frequent word pairs are more compositional. Three systems are described that use different measures of association (t-score, PMI, PMI) to classify word pair compositionality in a shared task. While the t-score performed best at identifying compositionality, PMI and frequency-based measures showed less success.
The document discusses replicability and reproducibility in ACL conferences. It argues that empirical papers should include software and data so results can be reproduced. An analysis found that most papers from ACL 2011 did not include software or data. Generally descriptions were incomplete and few papers allowed true reproducibility. The author calls for higher standards, weighting replicability more in reviews, and removing blind submissions to improve transparency.
This document summarizes research comparing different methods of measuring semantic similarity between concepts based on information content. It finds that using untagged text to derive information content, rather than the largest sense-tagged corpus, results in higher correlation with human judgments of similarity. Experiments showed no advantage to using sense-tagged text and that information content measures outperformed path-based measures, with estimates based just on taxonomy structure performing almost as well as using raw newspaper text.
The document discusses language independent methods for clustering similar contexts without using syntactic or lexical resources. It describes representing contexts as vectors of lexical features, reducing dimensionality, and clustering the vectors. Key methods include identifying unigram, bigram and co-occurrence features from corpora using frequency counts and association measures, and representing contexts in first or second order vectors based on feature presence.
The document summarizes a tutorial on word sense disambiguation (WSD) given at AAAI-2005. It introduces the problem of WSD, outlines different approaches including knowledge-intensive methods, supervised learning, minimally supervised and unsupervised learning. The tutorial aims to introduce WSD and persuade the audience to work on and apply WSD in their text applications.
The document describes language-independent methods for clustering similar contexts without using syntactic or lexical resources. It discusses representing contexts as vectors of lexical features and clustering them based on similarity. Feature selection involves identifying unigrams, bigrams, and co-occurrences based on frequency or association measures. Contexts can then be represented in first-order or second-order feature spaces and clustered. Applications include word sense discrimination, document clustering, and name discrimination.
This document provides an overview of a tutorial on word sense disambiguation (WSD). The tutorial aims to introduce the problem of WSD and various approaches, including knowledge-intensive methods, supervised learning approaches, and unsupervised learning. It covers the history of WSD, theoretical connections to other fields, practical applications, and an outline of the different parts of the tutorial.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
The document discusses the history and evolution of dictionaries from the first English dictionary in 1604 to modern computational approaches using natural language processing. It describes early dictionaries like Robert Cawdrey's Table Alphabeticall and Samuel Johnson's A Dictionary of the English Language. Later influential dictionaries included Noah Webster's American Dictionary of the English Language and the Oxford English Dictionary. The document proposes that natural language processing techniques like analyzing word frequencies, collocations, and measures of association could help identify emerging words and senses in new text, similar to the work of lexicographers in compiling dictionaries.
The document summarizes research on using lexical decision lists to screen Twitter users for depression and PTSD. It finds that a simple machine learning method using n-grams of varying length up to 6 words and binary weighting achieved the best results. Emoticons and emojis were strong indicators. The top features indicating depression included terms expressing sadness, while PTSD indicators included abbreviations and URLs. It suggests self-reporting of conditions may indicate something else requiring discussion.
Poster presented at the Semeval 2015 workshop. Our system clustered words based on their contexts in order to identify their underlying meanings or senses.
This document provides an overview of what it would be like to complete a Master's thesis under Dr. Ted Pedersen. It discusses that research involves asking interesting questions about the world and conducting experiments to answer those questions. Dr. Pedersen's research interests include natural language processing tasks like word sense disambiguation, semantic similarity, and collocation discovery. To succeed, a student needs enthusiasm for research, strong writing skills, and the ability to work independently while communicating regularly with Dr. Pedersen. Previous students have explored various NLP topics and many have gone on to PhD programs. The reading provided is intended to assess the student's understanding and interest in Dr. Pedersen's research areas.
This document summarizes a tutorial on measuring the similarity and relatedness of concepts. It discusses the distinction between semantic similarity and relatedness. It describes several common measures of similarity that use information from ontologies, such as path-based measures, measures that incorporate path and depth, and measures that incorporate information content. It also discusses measures of relatedness that can be used for concepts that are not connected by ontological relations, such as definition-based measures and measures based on gloss vectors constructed from corpus data. Experimental results generally show that gloss vector measures perform best, followed by definition-based measures, with path-based measures performing the worst.
Some thoughts on what it's like to do a Master's thesis with me, including general ideas about research, my research interests, and a few suggestions as to what will lead to success
This document describes UMLS::Similarity, an open source software that measures the semantic similarity or relatedness of biomedical terms from the Unified Medical Language Systems (UMLS). It provides several measures to quantify similarity/relatedness based on the hierarchical structure and definitions of terms in the UMLS. The software can be used via command line, API, or web interface and has been used in applications like word sense disambiguation.
The document discusses word sense induction systems developed at the University of Minnesota Duluth that were used to cluster web search results. The systems represented web snippets using second-order co-occurrences and were evaluated in Task 11 of SemEval-2013. The best performing system (Sys1) used more data in the form of web-like text and achieved an F-10 score of 46.53, outperforming systems that used larger amounts of out-of-domain news text. Future work could look at augmenting data by expanding snippets and using more web-based resources like Wikipedia.
These are the slides for a talk given at the University of Alabama, Birmingham on April 19, 2013. The title of the talk is "Measuring Similarity and Relatedness in the Biomedical Domain : Methods and Applications"
Measuring Semantic Similarity and Relatedness in the Biomedical Domain : Methods and Applications - presented Feb 21, 2012 as a webinar to the Mayo Clinic BMI group.
The document summarizes a tutorial on measuring semantic similarity and relatedness between medical concepts. It introduces different types of measures, including path-based measures, measures using information content that incorporate concept specificity, and measures of relatedness that use definition overlaps or corpus co-occurrence information. The tutorial aims to explain the distinction between similarity and relatedness, describe available measures, and how to evaluate and apply them in clinical natural language processing tasks.
The document describes experiments conducted to evaluate measures of association for identifying the compositionality of word pairs. It discusses two hypotheses: 1) word pairs with higher association scores are less compositional, and 2) more frequent word pairs are more compositional. Three systems are described that use different measures of association (t-score, PMI, PMI) to classify word pair compositionality in a shared task. While the t-score performed best at identifying compositionality, PMI and frequency-based measures showed less success.
The document discusses replicability and reproducibility in ACL conferences. It argues that empirical papers should include software and data so results can be reproduced. An analysis found that most papers from ACL 2011 did not include software or data. Generally descriptions were incomplete and few papers allowed true reproducibility. The author calls for higher standards, weighting replicability more in reviews, and removing blind submissions to improve transparency.
This document summarizes research comparing different methods of measuring semantic similarity between concepts based on information content. It finds that using untagged text to derive information content, rather than the largest sense-tagged corpus, results in higher correlation with human judgments of similarity. Experiments showed no advantage to using sense-tagged text and that information content measures outperformed path-based measures, with estimates based just on taxonomy structure performing almost as well as using raw newspaper text.
The document discusses language independent methods for clustering similar contexts without using syntactic or lexical resources. It describes representing contexts as vectors of lexical features, reducing dimensionality, and clustering the vectors. Key methods include identifying unigram, bigram and co-occurrence features from corpora using frequency counts and association measures, and representing contexts in first or second order vectors based on feature presence.
The document summarizes a tutorial on word sense disambiguation (WSD) given at AAAI-2005. It introduces the problem of WSD, outlines different approaches including knowledge-intensive methods, supervised learning, minimally supervised and unsupervised learning. The tutorial aims to introduce WSD and persuade the audience to work on and apply WSD in their text applications.
The document describes language-independent methods for clustering similar contexts without using syntactic or lexical resources. It discusses representing contexts as vectors of lexical features and clustering them based on similarity. Feature selection involves identifying unigrams, bigrams, and co-occurrences based on frequency or association measures. Contexts can then be represented in first-order or second-order feature spaces and clustered. Applications include word sense discrimination, document clustering, and name discrimination.
This document provides an overview of a tutorial on word sense disambiguation (WSD). The tutorial aims to introduce the problem of WSD and various approaches, including knowledge-intensive methods, supervised learning approaches, and unsupervised learning. It covers the history of WSD, theoretical connections to other fields, practical applications, and an outline of the different parts of the tutorial.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Duluth at Semeval 2017 Task 7 - Puns upon a Midnight Dreary, Lexical Semantics for the Weak and Weary
1. Homographic Puns
Task 1 Task 2 Task 3
N=9 N=15 N=8
0
0.2
0.4
0.6
0.8
1
0.87
0.66
0.16
0.83
0.37
0.44
0.16
High
Duluth 1
Duluth 2
F1
Detecting & Interpreting Puns Heterographic Puns
●
Does a pun occur anywhere in the given
sentence? (Task 1)
●
In those sentences with a pun, which word
is being punned? (Task 2)
●
In those sentences with a pun, which two
meanings of the punned word are being
invoked? (Task 3)
– Punned word and possible senses must
be known to WordNet.
Task 1 Task 2 Task 3
N=7 N=11 N=6
0
0.2
0.4
0.6
0.8
1
0.84
0.8
0.08
0.8
0.18
0.03
0.53
High
Duluth 1
Duluth 2
F1
●
The thief who stole from the library was
quickly booked.
●
A horse is a very stable animal.
●
That old statistician is really mean!!
●
The dog who played baseball always got
walked.
●
He recommended the restaurant for
brunch with no reservations.
●
The past, present, and future walked
into a bar. It was tense.
●
The hypnotist who went out of business
just needed a few suggestions.
Duluth at Semeval-2017 Task 7 :
Puns upon a Midnight Dreary,
Lexical Semantics for the Weak and Weary
●
I climbed that mountain, Tom alleged.
●
The cobbler seemed like a good sole.
●
Diets are for people who are thick and
tired of it all.
●
His candy collection was in mint condition.
●
His wife went home to mutter.
●
Old tree surgeons never die, they just take
a final bough.
Heterographic ResultsHomographic Results
Pun Detection as WSD
●
Assign senses to words, identify the words
with multiple valid possible meanings and
then maybe, just maybe, those are puns.
●
Context could be truly ambiguous or
under-specified, but many contexts have a
single assignment of senses.
●
SenseRelate Word Sense Disambiguation
●
Premise is to find the senses of words
that are most related to each other in a
context, then those senses should be
assigned to the words
●
http://senserelate.sourceforge.net
●
Does a pun occur anywhere in the given
sentence? (Task 1)
●
In those sentences with a pun, which word
is being punned? (Task 2)
●
In those sentences with a pun, which two
meanings of the punned word are being
invoked? (Task 3)
– Punned word and possible senses must
be known to WordNet.
Ted Pedersen
University of Minnesota, Duluth
tpederse@d.umn.edu
http://www.d.umn.edu/~tpederse
Methods
●
Task 1 – WordNet::SenseRelate::AllWords w/
various measures & window sizes, if results
are different then there is a pun.
●
Task 2 – Last word that has changed senses
is the pun. Duluth 2 just chooses last word.
●
Task 3 – WordNet::SenseRelate::TargetWord
with local and global settings for different
window sizes to identify senses of punned
word. For heterographic puns :
●
Duluth 1 uses all WordNet words within 1
edit distance as candidates.
●
Duluth 2 uses DataMuse API to find
rhyming words, sound and spell alikes and
synonyms.
●
Future Work? Better finding of candidates
for heterographic puns and use of language
models in addition to relatedness measures.