The document discusses various natural language processing (NLP) techniques including implementing search, document level analysis, sentence level analysis, and concept extraction. It provides details on tokenization, word normalization, stop word removal, stemming, evaluating search results, parsing and part-of-speech tagging, entity extraction, word sense disambiguation, concept extraction, dependency analysis, coreference, question parsing systems, and sentiment analysis. Implementation details and useful tools are mentioned for various techniques.
This is a Context based stemming or semantic stemming. It even goes beyond stemming hence it is named as Personalized derivative. User can even customize the output based on its domain by integrating ontologies .
Natural Language Processing reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage.
• What is Natural Language Processing?
• How & where to use NLP
• NLP for information retrieval
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
The objective of this workshop is to show how natural language processing applied in modern applications such as Google Search, Apple Siri, Bing Translator and etc. During the workshop we will go through history if natural language processing, talk about typical problems, consider classical approaches and methods, and compare them with state-of-the-art deep learning techniques.
Author: Rudolf Eremyan
Email: eremyan.rudolf@gmail.com
Phone: +995599607066
LinkedIn: https://www.linkedin.com/in/rudolferemyan/
DataFest Tbilisi 2017 website: https://datafest.ge
September 2021: Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
This is a Context based stemming or semantic stemming. It even goes beyond stemming hence it is named as Personalized derivative. User can even customize the output based on its domain by integrating ontologies .
Natural Language Processing reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage.
• What is Natural Language Processing?
• How & where to use NLP
• NLP for information retrieval
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
The objective of this workshop is to show how natural language processing applied in modern applications such as Google Search, Apple Siri, Bing Translator and etc. During the workshop we will go through history if natural language processing, talk about typical problems, consider classical approaches and methods, and compare them with state-of-the-art deep learning techniques.
Author: Rudolf Eremyan
Email: eremyan.rudolf@gmail.com
Phone: +995599607066
LinkedIn: https://www.linkedin.com/in/rudolferemyan/
DataFest Tbilisi 2017 website: https://datafest.ge
September 2021: Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
Natural language processing with python and amharic syntax parse tree by dani...Daniel Adenew
Natural Language Processing is an interrelated disincline adding the capability of communicating as human beings to Computerworld. Amharic language is having much improvement over time thanks to researcher at PHD, MSC level at AAU. Here , I have tried to study and come up a limited scope solution that does syntax parsing for Amharic language and draws syntax parse trees using Python!!
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
NLTK - Natural Language Processing in Pythonshanbady
For full details, including the address, and to RSVP see: http://www.meetup.com/bostonpython/calendar/15547287/ NLTK is the Natural Language Toolkit, an extensive Python library for processing natural language. Shankar Ambady will give us a tour of just a few of its extensive capabilities, including sentence parsing, synonym finding, spam detection, and more. Linguistic expertise is not required, though if you know the difference between a hyponym and a hypernym, you might be able to help the rest of us! Socializing at 6:30, Shankar's presentation at 7:00. See you at the NERD.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
Natural language processing with python and amharic syntax parse tree by dani...Daniel Adenew
Natural Language Processing is an interrelated disincline adding the capability of communicating as human beings to Computerworld. Amharic language is having much improvement over time thanks to researcher at PHD, MSC level at AAU. Here , I have tried to study and come up a limited scope solution that does syntax parsing for Amharic language and draws syntax parse trees using Python!!
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
NLTK - Natural Language Processing in Pythonshanbady
For full details, including the address, and to RSVP see: http://www.meetup.com/bostonpython/calendar/15547287/ NLTK is the Natural Language Toolkit, an extensive Python library for processing natural language. Shankar Ambady will give us a tour of just a few of its extensive capabilities, including sentence parsing, synonym finding, spam detection, and more. Linguistic expertise is not required, though if you know the difference between a hyponym and a hypernym, you might be able to help the rest of us! Socializing at 6:30, Shankar's presentation at 7:00. See you at the NERD.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
Mining Opinion Features in Customer ReviewsIJCERT JOURNAL
Now days, E-commerce systems have become extremely important. Large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. Client generated information and especially item reviews are significant sources of data for consumers to make informed buy choices and for makers to keep track of customer’s opinions. It is difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, mining product reviews has become a hot research topic and prior researches are mostly based on pre-specified product features to analyse the opinions. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to raw customer reviews and keywords can be extracted. This paper presents a survey on the techniques used for designing software to mine opinion features in reviews. Elven IEEE papers are selected and a comparison is made between them. These papers are representative of the significant improvements in opinion mining in the past decade.
2. Text Retrieval and Search
• Implementing Search
• Evaluating Search Results
NLP - Document Level Analysis
• Parsing and Part of Speech Tagging • Entity Extraction
• Word Sense Disambiguation
• Concept Extraction
• Concept Polarity ( positive, negative , objective)
NLP - Sentence Level Analysis
• Dependency Analysis and Coreference
• Example Question Parsing System
• SentimentAnalysis
} In progress
3. Implementing search
1. Tokenization
For each document, we split it into paragraphs, split paragraphs into sentences and
sentences into words. Attempt possible spell corrections.
2. Word Normalisation
Index text and query terms have same form e.g. match U.S.A and USA, Usually lower
cased
3. Stop word Removal
An optional step where a predefined list of stop words are removed. More important for
small corpuses
4. Stemming - Reduce terms to their stems
4.1 Language dependent - in English, every word has 2 parts, the stem and the affix
4.2 automate(s), automatic, automation => automat, plural forms like cats => cat
4.3 The “stem” may not be an actual word for e.g. consolidating => consolid
Evaluating Search Results
1. High Precision
2. High Recall
3. FMeasure
4. Extracting Concepts From Text
We apply various NLP techniques to analyze the contents of a document.
Some example are: Mentions of people, places, locations etc.
Central Themes or concepts in the document
This is different from search
Search follows a pull model where the users take initiative in querying the
system for relevant documents.
In concept extraction, we can infer abstract concepts from text and push it
to interested users. We may also be able to infer the concepts a user is
interested in based on the content they consume.
5. Sentence Segmentation
1. Periods are ambiguous - Abbreviations, decimals etc. !, ? - Less
ambiguous
2. Classifier - rules (using case, punctuation rules etc.), ML etc.
3. StanfordNLP sentence detection and tokenizer Refer :
http://nlp.stanford.edu/software/tokenizer.shtml
Trained on Penn Bank dataset and is hence suited towards more
formal english.
4. OpenNLP has a sentence detection and tokenizer as well.
Refer: https://opennlp.apache.org https://github.com/dpdearing/nlp
5. Elasticsearch tokenizer , ES lacks sentence detection but provides
various
tokenizer for natural languages
Refer : https://www.elastic.co/guide/en/elasticsearch/guide/current/languages.html
All these libraries perform pretty well for English for Evature integration
Elasticsearch is the way for general NLP an evaluation of other libs are
required as well
Part of Speech Tagging
6. Named Entity Recognition
Named Entity Recognition is the NLP task of recognizing proper nouns in a
document.
Named Entity Recognition consists of three steps:
1. Spotting: Statistical model pre-trained on well known corpus data help
us “spot” entities in the text.
2. Disambiguation: Once spots are found, we may need to disambiguate
them (for e.g. there are multiple entities with the same name and the
correct url needs to be retrieved)
3. Filtering - remove named entities whose types we are not interested in
or entities that have very few links pointing to them.
At the end of NER, we get back a set of url of resources that were
referenced
e.g. I go to school at <ORGANIZATION>Stanford
University</ORGANIZATION>, which is located in
<LOCATION>California</LOCATION>.
Useful tools
1. While using elasticsearch one can leverage Dbpedia Spotlight, it is an
API that can be used to perform all 3 steps of NER Refer: https://github.com/dbpedia-
7. Concept Extraction
Word sense Disambiguation
1. For many words, multiple senses of the word exists based on the context. For e.g. there
are multiple senses for the word “bank” (even within the same part of speech)
2. Extremely difficult for Computers.. A combination of context and common sense
information make this quite easy for humans.
3.Word Sense Disambiguation can be useful for
3.1 Machine translation between languages (surface form loses value during translation
because the only thing that matters is the sense of the word)
3.2 Information Retrieval - Correct interpretation of the query. However this can be
overcome by providing enough terms to only retrieve relevant documents.
3.3 Automatic annotation of text
3.4 Measuring semantic relatedness between documents.
Supervised vs. Unsupervised WSD
If we have training data, word sense disambiguation reduces to a classification problem.
Additional training data may be supplied in the form of dictionary definitions, ontologies such
as Medical Subject Headings (MeSH), or lexical resources like WordNet.
If there is no training data, word sense disambiguation is a clustering problem. Hierarchical
clusterings may make sense; the dictionaries sited above break meanings of the word "run"
down into senses and sub-senses.
Useful tools
Knowledge inventories -> Wordnet*, Wikipedia, Freebase, ConceptNet
Collection of processing Algos —> Lingpipe, Deepdive
ML libs —> Most of Algos are with Lingpipe/Deepdive, Weka also has some extended libs
*WordNet is a hierarchically organized lexical database widely used in NLP applications. Started at Princeton in 1985.
8. Concept Graph
WordNet does not capture any common sense information. For e.g. bank
(financial institution) and money do not have a close relationship in WordNet.
It is possible to use other resource like ConceptNet that map common sense
knowledge to WordNet (and ontologies like dbpedia). For e.g. we can download
mappings for concepts like Money, Love, Sports, Family etc.
Another option is to deploy a custom concept graph:
Deploy WordNet onto a Graph database that is Neo4j. That forms the base graph.
Deploy custom concept mapping to the WordNet synsets. Custom concept
mapping can be created in form of ontologies using tools like Protege
Add mappings for relevant wikipedia (dbpedia) categories
Concept Polarity
Opinion mining (OM – also known as “sentiment classification”) is a recent
subdiscipline at the crossroads of infor- mation retrieval and computational
linguistics which is concerned not with the topic a text is about, but with the opin-
ion it expresses.
Sentiwordnet is an opensource tool for Opinion mining. E.g. “They are really
happy to be here” => happy#a#1 has a very positive polarity. Refer :
http://sentiwordnet.isti.cnr.it
10. Dependency Analysis and Coreference
• LexicalizedParser can provide a graph where the vertices are the
words and the edges are the grammatical relationships in a sentence.
Question Parsing System http://ceur-ws.org/Vol-1180/CLEF2014wn-QA-
XuEt2014.pdf
11. Thoughts• Explore textRank and query suggest based on that rank. Google
way
• Shallow nlp generally employed in text retrieval and search provide
good results for general search use cases.
• Deeper NLP involves semantic parsing, common sense interpolation
(both local and global knowledge bases) and tends to be harder.
• Deeper NLP is more practical after picking a specific domain for e.g.
medical records, legal documents etc.
• Sentiment Analysis (http://watson-um-demo.mybluemix.net)