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Stemming - Natural Language Processing With Python and NLTK p.3
 
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Another form of data pre-processing with natural language processing is called "stemming." This is the process where we remove word affixes from the end of words. The reason we would do this is so that we do not need to store the meaning of every single tense of a word. For example: Reader Reading Read Aside from tense, and even one of these is a noun, they all have the same meaning for their "root" stem (read). This way, we store one single value for the root stem of "read." Then, when we wish to learn more, we can look into the affixes that were on the end, like "ing" is an active word, or in the past, then you have reader as someone who reads... then just plain read as either past tense or current. sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 104273 sentdex
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 404462 sentdex
Stemming words from a sentence for text analysis - NLTK Python in Hindi #6
 
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Text Tutorial + Source Code - http://mycodingzone.net/videos/hindi/nlp-hindi-tutorial-6
Lemmatizing - Natural Language Processing With Python and NLTK p.8
 
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A very similar operation to stemming is called lemmatizing. The major difference between these is, as you saw earlier, stemming can often create non-existent words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary. A root lemma, on the other hand, is a real word. Many times, you will wind up with a very similar word, but sometimes, you will wind up with a completely different word. sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 51439 sentdex
Natural Language Processing Tutorial Part 2 | NLP Training Videos | Text Analysis
 
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Natural Language Processing Tutorial Part 2 | NLP Training Videos | Text Analysis https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=9LLs2I8_gQQ&campaign=youtube_channel&utm_source=youtube&utm_medium=NLP-part-2&utm_campaign=youtube_channel Hello and Welcome back to Data Science tutorials powered by Acadgild. In the previous video, we came across the introduction part of the natural language processing (NLP) which includes the hands-on part with tokenization, stemming, lemmatization, etc. If You have missed the previous video, kindly click the following link for the better understanding and continuation for the series. NLP Training Video Part 1 - https://www.youtube.com/watch?v=Na4ad0rqwQg In this tutorial, you will be able to learn, • What are the stop keywords and its importance in the process of text analysis? Before going to the core topic let’s understand the difference between Lemmatization and Stemming. Lemmatization Vs Stemming: Lemmatization: • Word representations have meaning • Takes more time than stemming • Use lemmatization when the meaning of words is important for analysis • For example, question answering application. Stemming: • Word representations may not have any meaning • Takes less time • Use stemming when the meaning of words is not important for analysis. • For example, spam detection Kindly go through the hands-on part to learn more about the usage of stop keywords in text analysis. Please like, share and subscribe the channel for more such videos. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 399 ACADGILD
Weighting by Term Frequency - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 14517 Udacity
Text Classification - Natural Language Processing With Python and NLTK p.11
 
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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 92817 sentdex
Topic Detection with Text Mining
 
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Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST. Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform. We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection. We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more! Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected] This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course
Views: 2428 KNIMETV
Text Mining Problems
 
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I would like to thank Lauren Briggs (Durban, South Africa) and Sean Pethybridge (Surf City, New Jersey) for giving voices to Laura, Saundra and Markus.
Views: 196 Fabio Stella
What is LEMMATISATION? What does LEMMATISATION mean? LEMMATISATION meaning & explanation
 
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What is LEMMATISATION? What does LEMMATISATION mean? LEMMATISATION meaning - LEMMATISATION pronunciation - LEMMATISATION definition - LEMMATISATION explanation - How to pronounce LEMMATISATION? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Lemmatisation (or lemmatization) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. In computational linguistics, lemmatisation is the algorithmic process of determining the lemma of a word based on its intended meaning. Unlike stemming, lemmatisation depends on correctly identifying the intended part of speech and meaning of a word in a sentence, as well as within the larger context surrounding that sentence, such as neighboring sentences or even an entire document. As a result, developing efficient lemmatisation algorithms is an open area of research. In many languages, words appear in several inflected forms. For example, in English, the verb 'to walk' may appear as 'walk', 'walked', 'walks', 'walking'. The base form, 'walk', that one might look up in a dictionary, is called the lemma for the word. The association of the base form with a part of speech is often called a lexeme of the word. Lemmatisation is closely related to stemming. The difference is that a stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech. However, stemmers are typically easier to implement and run faster. The reduced "accuracy" may not matter for some applications. In fact, when used within information retrieval systems, stemming improves query recall accuracy, or true positive rate, when compared to lemmatisation. Nonetheless, stemming reduces precision, or true negative rate, for such systems. For instance: 1. The word "better" has "good" as its lemma. This link is missed by stemming, as it requires a dictionary look-up. 2. The word "walk" is the base form for word "walking", and hence this is matched in both stemming and lemmatisation. 3. The word "meeting" can be either the base form of a noun or a form of a verb ("to meet") depending on the context; e.g., "in our last meeting" or "We are meeting again tomorrow". Unlike stemming, lemmatisation attempts to select the correct lemma depending on the context. Document indexing software like Lucene can store the base stemmed format of the word without the knowledge of meaning, but only considering word formation grammar rules. The stemmed word itself might not be a valid word: 'lazy', as seen in the example below, is stemmed by many stemmers to 'lazi'. This is because the purpose of stemming is not to produce the appropriate lemma – that is a more challenging task that requires knowledge of context. The main purpose of stemming is to map different forms of a word to a single form. As a rules-based algorithm, dependent only upon the spelling of a word, it sacrifices accuracy to ensure that, for example, when 'laziness' is stemmed to 'lazi', it has the same stem as 'lazy'. Morphological analysis of published biomedical literature can yield useful results. Morphological processing of biomedical text can be more effective by a specialised lemmatisation program for biomedicine, and may improve the accuracy of practical information extraction tasks.
Views: 944 The Audiopedia
Stop Words - Natural Language Processing With Python and NLTK p.2
 
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One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to "clean up" and prepare your data for analysis. One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with. The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 130887 sentdex
What Is Word Stemming?
 
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"KNOW ABOUT What Is Word Stemming LIST OF RELATED VIDEOS OF What Is Word Stemming IN THIS CHANNEL : What Is Word Stemming? https://www.youtube.com/watch?v=unVddWZ0I9c What Is The Process Of Production? https://www.youtube.com/watch?v=oDYAPIIaDJs What Is The Online System? https://www.youtube.com/watch?v=1ztJKZ8b1Xw What Is The STP Process? https://www.youtube.com/watch?v=CDUkrDoTZN4 What Is The Use Of Backup And Restore? https://www.youtube.com/watch?v=cC8erRA-XYc What Is Windows Backup? https://www.youtube.com/watch?v=qGeKE-S7s20 What Is The White Hat In Scandal? https://www.youtube.com/watch?v=c5FJIaJlMEk What Is The Role Of A Producer In A Movie? https://www.youtube.com/watch?v=bkfgKNW7zsA What Is The Use Of Using Keyword Prominence? https://www.youtube.com/watch?v=s-UE4m2cBNo What Is This Stemming From? https://www.youtube.com/watch?v=mkrS6wu_BLc"
Views: 223 sparky Facts
What is TEXT CORPUS? What does TEXT CORPUS mean? TEXT CORPUS meaning, definition & explanation
 
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What is TEXT CORPUS? What does TEXT CORPUS mean? TEXT CORPUS meaning - TEXT CORPUS definition - TEXT CORPUS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In linguistics, a corpus (plural corpora) or text corpus is a large and structured set of texts (nowadays usually electronically stored and processed). They are used to do statistical analysis and hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. A corpus may contain texts in a single language (monolingual corpus) or text data in multiple languages (multilingual corpus). Multilingual corpora that have been specially formatted for side-by-side comparison are called aligned parallel corpora. There are two main types of parallel corpora which contain texts in two languages. In a translation corpus, the texts in one language are translations of texts in the other language. In a comparable corpus, the texts are of the same kind and cover the same content, but they are not translations of each other. To exploit a parallel text, some kind of text alignment identifying equivalent text segments (phrases or sentences) is a prerequisite for analysis. Machine translation algorithms for translating between two languages are often trained using parallel fragments comprising a first language corpus and a second language corpus which is an element-for-element translation of the first language corpus. In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or POS-tagging, in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of tags. Another example is indicating the lemma (base) form of each word. When the language of the corpus is not a working language of the researchers who use it, interlinear glossing is used to make the annotation bilingual. Some corpora have further structured levels of analysis applied. In particular, a number of smaller corpora may be fully parsed. Such corpora are usually called Treebanks or Parsed Corpora. The difficulty of ensuring that the entire corpus is completely and consistently annotated means that these corpora are usually smaller, containing around one to three million words. Other levels of linguistic structured analysis are possible, including annotations for morphology, semantics and pragmatics. Corpora are the main knowledge base in corpus linguistics. The analysis and processing of various types of corpora are also the subject of much work in computational linguistics, speech recognition and machine translation, where they are often used to create hidden Markov models for part of speech tagging and other purposes. Corpora and frequency lists derived from them are useful for language teaching. Corpora can be considered as a type of foreign language writing aid as the contextualised grammatical knowledge acquired by non-native language users through exposure to authentic texts in corpora allows learners to grasp the manner of sentence formation in the target language, enabling effective writing. Text corpora are also used in the study of historical documents, for example in attempts to decipher ancient scripts, or in Biblical scholarship. Some archaeological corpora can be of such short duration that they provide a snapshot in time. One of the shortest corpora in time, may be the 15–30 year Amarna letters texts (1350 BC). The corpus of an ancient city, (for example the "Kültepe Texts" of Turkey), may go through a series of corpora, determined by their find site dates.
Views: 1226 The Audiopedia
Stemming Algoritma Nazef+Adriani
 
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Tutorial Text Mining
Views: 131 Ahmad Harmain
Words as Features for Learning - Natural Language Processing With Python and NLTK p.12
 
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For our text classification, we have to find some way to "describe" bits of data, which are labeled as either positive or negative for machine learning training purposes. These descriptions are called "features" in machine learning. For our project, we're just going to simply classify each word within a positive or negative review as a "feature" of that review. Then, as we go on, we can train a classifier by showing it all of the features of positive and negative reviews (all the words), and let it try to figure out the more meaningful differences between a positive review and a negative review, by simply looking for common negative review words and common positive review words. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 61955 sentdex
Stemming Words in Python NLTK
 
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Learn how to do stemming of text in Python NLTK.
Views: 2770 DevNami
Part of Speech Tagging - Natural Language Processing With Python and NLTK p.4
 
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Part of Speech tagging does exactly what it sounds like, it tags each word in a sentence with the part of speech for that word. This means it labels words as noun, adjective, verb, etc. PoS tagging also covers tenses of the parts of speech. This is normally quite the challenge, but NLTK makes this pretty darn simple! sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 109318 sentdex
NLP - Text Preprocessing and Text Classification (using Python)
 
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Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. "The hotel is really beautiful. Very nice and helpful service at the front desk." So we read that and we understand that is a positive review. As for the negative review, "We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn't fun for us." So, it's easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we'll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, "I don't really like", that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let's start with words and let's denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters. So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let's look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They're just written in such form that they don't have spaces.The Japanese language is a different story.
Views: 1426 Machine Learning TV
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 7496 LearnEveryone
Stemming to Consolidate Vocabulary
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 2366 Udacity
Stemmer Meaning
 
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Video shows what stemmer means. Software used to produce the stem from the inflected form of words.. Stemmer Meaning. How to pronounce, definition audio dictionary. How to say stemmer. Powered by MaryTTS, Wiktionary
Views: 66 ADictionary
WordNet  - Natural Language Processing With Python and NLTK p.10
 
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Part of the NLTK Corpora is WordNet. I wouldn't totally classify WordNet as a Corpora, if anything it is really a giant Lexicon, but, either way, it is super useful. With WordNet we can do things like look up words and their meaning according to their parts of speech, we can find synonyms, antonyms, and even examples of the word in use. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 62832 sentdex
Introduction to Text Analytics with R: Data Pipelines
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 3 of this video series provides an introduction to the video series and includes specific coverage: - Exploration of textual data for pre-processing “gotchas” - Using the quanteda package for text analytics - Creation of a prototypical text analytics pre-processing pipeline, including (but not limited to): tokenization, lower casing, stop word removal, and stemming. - Creation of a document-frequency matrix used to train machine learning models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K0c0 See what our past attendees are saying here: https://hubs.ly/H0f5JN90 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 15490 Data Science Dojo
Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 7 of this video series includes specific coverage of: - The trade-offs of expanding the text analytics feature space with n-grams. - How bag-of-words representations map to the vector space model (VSM). - Usage of the dot product between document vectors as a proxy for correlation. - Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. - How LSA is implemented using singular value decomposition (SVD). - Mapping new data into the lower dimensional SVD space. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JVc0 See what our past attendees are saying here: https://hubs.ly/H0f5K6Q0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 9521 Data Science Dojo
NLTK Text Processing 06 - Lemmas, Synonyms and Antonyms
 
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In this video I talk about Lemmas, Synonyms and Antonyms under WordNet Lemmas, Synonyms and Antonyms by Rocky DeRaze
Views: 1505 Rocky DeRaze
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 132890 Brandon Weinberg
Document Similarity and Clustering in RapidMiner
 
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This is part 4 of a 5 part video series on Text Mining using the free and open-source RapidMiner. This video describes how to calculate a term's TF-IDF score, as well as how to find similar documents using cosine similarity, and how to cluster documents using the K-Means algorithm.
Views: 47934 el chief
Build a Text Summarizer in Java
 
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Get the Code here : https://github.com/ajhalthor/text-summarizer Follow me on Twitter : https://twitter.com/ajhalthor Take a look at the original by Shlomi Babluki : http://thetokenizer.com/2013/04/28/build-your-own-summary-tool/ TRANSCRIPT OVERVIEW ALGORITHM 1. Take the full CONTENT and split it into PARAGRAPHS. 2. Split each PARAGRAPH into SENTENCES. 3. Compare every sentence with every other. This is done by Counting the number of common words and then Normalize this by dividing by average number of words per sentence. 4. These intermediate scores/values are stored in an INTERSECTION matrix 5. Create the key-value dictionary - Key : Sentence - Value : Sum of intersection values with this sentence 6. From every paragraph, extract the sentences with the highest score. 7. Sort the selected sentences in order of appearance in the original text to preserve content and meaning. And like that, you have generated a summary of the original text. CLASSES IN JAVA PROJECT 1. Sentence : The entire text is divided into a number of paragraphs and each paragraph is divided into a number of sentences. 2. Paragraph : Every paragraph has a number associated with it and an Array List of sentences. 3. Sentence Comparitor : Compare Sentence objects based on Score 4. SentenceComparatorForSummary : Compare Sentence objects based on position in text. 5. SummayTool : akes care of all the operations from extracting sentences to generating the summary. HOW IS MY SUMMARIZER BETTER THAN THE ORIGINAL ? My text summarizer selects number of sentences from a paragraph depending on the length. This is an improvement over the original text summarizer implementation that only selects 1 sentence per paragraph regardless of length. So, If the author decides to crunch everything into 1 paragraph, then only one sentence would be chosen. In the current implementation, we set it to accept several sentences for larger paragraphs. It delivers cogent summaries for general essays, reviews and publications. RUN THIS PROGRAM $ javac -d bin improved_summary.java $ java -classpath bin improved_summary
Views: 6337 CodeEmporium
Understanding Bag of Words Model - Hands On NLP using Python Demo
 
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This video is a part of the popular Udemy course on Hands-On Natural Language Processing (NLP) using Python. This course covers all the concepts of NLP along with proper implementations. If you are really interested in learning NLP make sure to check this course. Here is a special coupon for you with 90% off bonus: https://www.udemy.com/hands-on-natural-language-processing-using-python/?couponCode=SPECIAL007
Views: 954 Deep Coding
Query Types, Stemming, Wildcard, Range Searching, and more
 
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http://www.perfectsearchcorp.com -- Ken Ebert, CTO at Perfect Search, talks about the different types of queries that can be done with the Perfect Search search engine.
Views: 255 PerfectSearchCorp
NLTK Text Processing 05 - Synsets, Hypernyms and Hyponyms
 
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In this video I talk about WordNet, Sysnets, Hypernyms and Hyponyms Synsets in NLTK by Rocky DeRaze
Views: 2072 Rocky DeRaze
Chinking - Natural Language Processing With Python and NLTK p.6
 
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Chinking is a part of the chunking process with natural language processing with NLTK. A chink is what we wish to remove from the chunk. We define a chink in a very similar fashion compared to how we defined the chunk. The reason why you may want to use a chink is when your chunker is getting almost everything you want, but is also picking up some things you don't want. You could keep adding chunker rules, but it may be far easier to just specify a chink to remove from the chunk. sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 52496 sentdex
What Is The Plural Of The Word Corpus?
 
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Merriam webster says the only plural form is corpora, for all senses of word sep 20, 2009 latin 'corpus' (which means 'body' in english) frequently used to designate a collection messages, either ham or spam. Arguably, english will allow the use of 'corpuses', but it looks and sounds a little odd corpus definition, large or complete collection writings entire old linguistics. Corpora lutea is in the lower 50. If your text file is c('taste', 'tastes', 'tasting'). Corpora lutea definition and meaning 21 the quranic arabic corpus word by grammar, syntax r create other forms[noun,adjective,plural,verb everything] of a. Drunk texts, squad goals, and brewer's droop an oxford dictionaries update corpus definition, meaning, what is a collection of written or spoken material stored on computer us k rp s plural corpora r. Corpus definition for english language learners from merriam corpus. Corpora lutea definition corpus luteum. You can use the package tm and its stemming capabilities. A body of utterances, as words or sentences, assumed to be (plural corpora), late 14c. Meaning, pronunciation, translations and examplescollins english used rarely. Corpus (plural corpora or corpuses). Definition of corpus by merriam webster dictionary url? Q webcache. Define corpus a collection of writings, conversations, speeches, etc. What is the plural of corpus? Word hippodifferences when should i use 'corpuses' over 'corpora'? English pluralofcorpus spamassassin wiki apache wikidefine corpus at dictionary. Pluralofcorpus spamassassin wiki apache wikidefinition of corpus by merriam webster. You can create a corpus taking assisted approach and looking up both 'syllabuses' 'syllabi' in the some of us may treat syllabus as latin or latinised word pluralise it to syllabi, though my experience most people will regard bit many finnish students use singular, whereas plural form researches. For example, status and hiatus only change the length of final vowelcorpus goes to. From latin corpus, literally 'body' (see corporeal) definition of corpus written for english language learners the merriam webster learner's plural corpora ko p r word day looking online in medical dictionary? Corpus explanation (k r'p s, is, ), [ta] this is corpora, not corpi may 24, 2010 singular, pluralgenitive, corporis, corporumaccusative, oxford dictionaries year 2016 dictionary 20update. Contractions) stem the words in corpus corpuses a less commonly used plural form of keyword whose frequency is unusually high (positive keywords) or low (negative nov 17, 1999 this mis inflects many. Which is the correct plural of syllabus syllabuses or syllabi? Quoramkfs misc text mining githubkent state university. I've come into a situation where i need to use the plural form of corpus, but i'm bit confused about which. Pluralofcorpus spamassassin wiki apache wiki corpus. With the help of corpus we will try and find out whether function to combine single plural variants in a term frequency vector wc_corpus tm_map
What is corpus
 
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tutorial
Views: 537 soso1404soso
What Is This Stemming From?
 
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"OBSERVE What Is This Stemming From LIST OF RELATED VIDEOS OF What Is This Stemming From IN THIS CHANNEL : What Is This Stemming From? https://www.youtube.com/watch?v=mkrS6wu_BLc What Is The Meaning Of Online? https://www.youtube.com/watch?v=02Jdsk0XbSA What Is The Identifier In C++? https://www.youtube.com/watch?v=lhfcuSWyho0 What Is The Process Of Production? https://www.youtube.com/watch?v=oDYAPIIaDJs What Is The Role Of A Producer In A Movie? https://www.youtube.com/watch?v=bkfgKNW7zsA What Is The White Hat In Scandal? https://www.youtube.com/watch?v=c5FJIaJlMEk What Is The Use Of Backup And Restore? https://www.youtube.com/watch?v=cC8erRA-XYc What Is The Online System? https://www.youtube.com/watch?v=1ztJKZ8b1Xw What Is The STP Process? https://www.youtube.com/watch?v=CDUkrDoTZN4 What Is The Use Of Using Keyword Prominence? https://www.youtube.com/watch?v=s-UE4m2cBNo"
Views: 78 sparky Facts
SAP HANA Academy - Text Analysis: Grammatical Role Analysis [SPS 11]
 
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In SPS11, a new feature called Grammatical Role Analysis was introduced which is an optional analyzer for English that identifies syntactic relationships between elements of a sentence in the form of subject–verb–object expressions, commonly known as ‘triples’. In this video tutorial, Tahir Hussain Babar (Bob) gives an introduction into how it works. Scripts ; https://github.com/saphanaacademy/TextAnalysis_Search_Mining/blob/master/TextAnalysis_SPS11.txt Thank you for watching. Video by the SAP HANA Academy. SOCIAL MEDIA Feel free to connect with us at the links below: LinkedIn: https://linkedin.com/saphanaacademy Twitter: https://twitter.com/saphanaacademy Facebook: https://www.facebook.com/saphanaacademy/ Google+: https://plus.google.com/u/0/111935864030551244982 Github: https://github.com/saphanaacademy
Views: 666 SAP HANA Academy
Urdu Stemmer
 
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How Urdu Stemmer works Final year Project
Views: 236 asad ali
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: Twitter: Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: Glove: Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of Subscribe & More Videos: https://goo.gl/bxbhNu Thank for watching, Please Like Share And SUBSCRIBE!!! #deeplearningnlp, #recurrentnets
Views: 136 Ngoc Hieu
NLTK Text Processing 07 - Wu Palmer Similarity
 
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In this video I talk about Wu Palmer Similarity, which can be used to find out if two words are similar and if so, how similar. Wu Palmer Similarity in NLTK by Rocky DeRaze
Views: 1233 Rocky DeRaze
How to say "lemmatization"! (High Quality Voices)
 
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Watch in this video how to say and pronounce "lemmatization"! The video is produced by yeta.io
Views: 18 WordBox

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