PROJECT MANAGEMENT
WRIKE
http://try.wrike.com/free-all-in-one-collaboration-project-management-software/
Wrike, Inc. 200 W. Evelyn Avenue Suite 110 Mountain View, CA 94041
Friday, January 23, 2015
PRODUCT LIFECYCLE MANAGEMENT & PROJECT MANAGEMENT
PRODUCT LIFECYCLE MANAGEMENT
PROJECT MANAGEMENT
WRIKE
http://try.wrike.com/free-all-in-one-collaboration-project-management-software/
Wrike, Inc. 200 W. Evelyn Avenue Suite 110 Mountain View, CA 94041
PROJECT MANAGEMENT
WRIKE
http://try.wrike.com/free-all-in-one-collaboration-project-management-software/
Wrike, Inc. 200 W. Evelyn Avenue Suite 110 Mountain View, CA 94041
Wednesday, December 10, 2014
NLP USE CASES AND TESTING
word2vec 2013
project page: https://code.google.com/p/word2vec/
core research:
Tomas Mikolov
Efficient Estimation of Word Representations in Vector Space
(http://goo.gl/ZvBp8F)
http://arxiv.org/pdf/1301.3781.pdf
follow up -
Distributed Representations of Words and Phrases
and their Compositionality
http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
Distributed Representations of Sentences and Documents
Quoc Le, Tomas Mikolov
http://cs.stanford.edu/~quocle/paragraph_vector.pdf
http://www.i-programmer.info/news/105-artificial-intelligence/6264-machine-learning-applied-to-natural-language.html
Representing words as high dimensional vectors
https://plus.google.com/+ResearchatGoogle/posts/VwBUvQ7PvnZ
http://radimrehurek.com/2014/02/word2vec-tutorial/
Deep learning via word2vec’s “skip-gram and CBOW models”, using either hierarchical softmax or negative sampling [1] [2].
http://radimrehurek.com/gensim/models/word2vec.html
http://mfcabrera.com/research/2013/11/14/word2vec-german.blog.org/
DEEPLEARNING4J
core research:
GloVe: Global Vectors for Word Representation
http://stanford.edu/~jpennin/papers/glove.pdf
We provide the source code for the model as
well as trained word vectors at
http://nlp.stanford.edu/projects/glove/
Best word vectors so far? http://stanford.edu/~jpennin/papers/glove.pdf … 11% more accurate than word2vec, fast to train, statistically efficient, good task accuracy
Semantic Compositionality Through Recursive Matrix-Vector Spaces
Download Paper
SocherHuvalManningNg_EMNLP2012.pdf
Download Code
Relation Classification
relationClassification.zip (525MB) - All training code and testing code with trained models for new data. External packages (parser, tagger) included and the whole pipeline should run with one script. This is the package if you just want to use the best model to classify your relations.
relationClassification-No-MVRNN-models.zip (103MB) - All training code and testing code but WITHOUT trained models. External packages (parser, tagger) included. Here you need to first run the full training script, which will take a few hours to run.
relationClassification-Only-code.zip (170kB) - All training code and testing code but WITHOUT trained models, external packages, word vectors or anything else. This package includes only the code so you can study the algorithm.
core research:
Tomas Mikolov
Efficient Estimation of Word Representations in Vector Space
(http://goo.gl/ZvBp8F)
http://arxiv.org/pdf/1301.3781.pdf
follow up -
Distributed Representations of Words and Phrases
and their Compositionality
http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
word2vec Explained: Deriving Mikolov et al.’s
Negative-Sampling Word-Embedding Method
Yoav Goldberg and Omer Levy
{yoav.goldberg,omerlevy}@gmail.com
February 14, 2014
Distributed Representations of Sentences and Documents
Quoc Le, Tomas Mikolov
http://cs.stanford.edu/~quocle/paragraph_vector.pdf
blogs and tutorials:
Representing words as high dimensional vectors
https://plus.google.com/+ResearchatGoogle/posts/VwBUvQ7PvnZ
http://radimrehurek.com/2014/02/word2vec-tutorial/
Deep learning via word2vec’s “skip-gram and CBOW models”, using either hierarchical softmax or negative sampling [1] [2].
http://radimrehurek.com/gensim/models/word2vec.html
http://mfcabrera.com/research/2013/11/14/word2vec-german.blog.org/
DEEPLEARNING4J
GloVe 2014
project page: http://nlp.stanford.edu/projects/glove/core research:
GloVe: Global Vectors for Word Representation
http://stanford.edu/~jpennin/papers/glove.pdf
We provide the source code for the model as
well as trained word vectors at
http://nlp.stanford.edu/projects/glove/
Best word vectors so far? http://stanford.edu/~jpennin/papers/glove.pdf … 11% more accurate than word2vec, fast to train, statistically efficient, good task accuracy
RNN trained word vectors 2012
http://www.socher.org/index.php/Main/SemanticCompositionalityThroughRecursiveMatrix-VectorSpacesSemantic Compositionality Through Recursive Matrix-Vector Spaces
Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them.
Download Paper
SocherHuvalManningNg_EMNLP2012.pdf
Download Code
Relation Classification
relationClassification.zip (525MB) - All training code and testing code with trained models for new data. External packages (parser, tagger) included and the whole pipeline should run with one script. This is the package if you just want to use the best model to classify your relations.
relationClassification-No-MVRNN-models.zip (103MB) - All training code and testing code but WITHOUT trained models. External packages (parser, tagger) included. Here you need to first run the full training script, which will take a few hours to run.
relationClassification-Only-code.zip (170kB) - All training code and testing code but WITHOUT trained models, external packages, word vectors or anything else. This package includes only the code so you can study the algorithm.
How to measure quality of the word vectors
Several factors influence the quality of the word vectors:
- amount and quality of the training data
- size of the vectors
- training algorithm
The quality of the vectors is crucial for any application. However, exploration of different hyper-parameter settings for complex tasks might be too time demanding. Thus, we designed simple test sets that can be used to quickly evaluate the word vector quality.
For the word relation test set described in [1], see ./demo-word-accuracy.sh, for the phrase relation test set described in [2], see ./demo-phrase-accuracy.sh. Note that the accuracy depends heavily on the amount of the training data; our best results for both test sets are above 70% accuracy with coverage close to 100%.
test metric, tests GloVe vs Word2Vec
On the importance of comparing apples to apples: a case study using the GloVe model
Yoav Goldberg, 10 August 2014
links from word2vec paper http://arxiv.org/pdf/1301.3781.pdf:
The test set is available at http://www.fit.vutbr.cz/˜imikolov/rnnlm/word-test.v1.txt
http://ronan.collobert.com/senna/
http://metaoptimize.com/projects/wordreprs/
http://www.fit.vutbr.cz/˜imikolov/rnnlm/
http://ai.stanford.edu/˜ehhuang/
Microsoft Research Sentence Completion Challenge
http://research.microsoft.com/en-us/um/people/cburges/tech_reports/MSR-TR-2011-129.pdf
G. Zweig, C.J.C. Burges. The Microsoft Research Sentence Completion Challenge, Microsoft
Research Technical Report MSR-TR-2011-129, 2011.
Appendix: Full List of Training Data
The Microsoft Sentence Completion Challenge has been recently introduced as a task for advancing language modeling and other NLP techniques [32]. This task consists of 1040 sentences, where one word is missing in each sentence and the goal is to select word that is the most coherent with the rest of the sentence, given a list of five reasonable choices.
C.J.C. Burges papers:
http://research.microsoft.com/en-us/um/people/cburges/pubs.htm
The test set is available at http://www.fit.vutbr.cz/˜imikolov/rnnlm/word-test.v1.txt
http://ronan.collobert.com/senna/
http://metaoptimize.com/projects/wordreprs/
http://www.fit.vutbr.cz/˜imikolov/rnnlm/
http://ai.stanford.edu/˜ehhuang/
Microsoft Research Sentence Completion Challenge
http://research.microsoft.com/en-us/um/people/cburges/tech_reports/MSR-TR-2011-129.pdf
G. Zweig, C.J.C. Burges. The Microsoft Research Sentence Completion Challenge, Microsoft
Research Technical Report MSR-TR-2011-129, 2011.
Appendix: Full List of Training Data
The Microsoft Sentence Completion Challenge has been recently introduced as a task for advancing language modeling and other NLP techniques [32]. This task consists of 1040 sentences, where one word is missing in each sentence and the goal is to select word that is the most coherent with the rest of the sentence, given a list of five reasonable choices.
C.J.C. Burges papers:
http://research.microsoft.com/en-us/um/people/cburges/pubs.htm
CORTICAL
Potential Projects in Fashion AI
Fashion Marketing and Digital Media Group on LinkedIn.
https://alicemitchellx.wordpress.com/2014/12/09/the-perspective-media-interview/http://box-of-style.blogspot.com/2014/12/recommended-read-imagine-world-where.html
If you would like to submit predictions for project related to the future of fashion please let me know. Welcome your feedback and questions.
Paul
https://www.stylewe.com/
StyleWe is an online fashion shopping platform featuring independent fashion designers. We are committed to providing shoppers with original, high quality, and exclusive fashion products from independent designers.
By working with cutting edge independent fashion designers from around the
world, and combining them with our high quality production and digital marketing
capabilities, we will turn the fashion designers’ dreams into reality by providing high fashion to customers worldwide.
Rather than just an online shopping store, we would like to create a
community which will be shared by both designers and customers. The community will enable
all parties to communicate, share ideas, and recognize each other. It would not only provide instant feedback for fashion designers when launching new concepts or products, but would also allow customers to share their shopping experiences
and fashion dreams.
We bring together designers and fashion covering many different styles. We hope
that every one of our customers will find their own unique and exclusive
designer fashions at StyleWe.
We believe the fashion trend should not be controlled by the few, but rather
be guided by the collective actions of every designer and fashion consumer. At
StyleWe, our goal is to empower designers so that they no longer feel hidden behind the brand, but are able to proactively communicate
directly with their customers throughout the entire fashion life cycle.
We believe fashion should be personal and diversified. Fashion designers
should not cater exclusively to the rich and famous. We have dedicated ourselves
to enabling talented fashion designers to build their own brands and achieve their
dream. Together with our designers, we will deliver high quality designer
fashions to everyone
Monday, December 8, 2014
Cortical.io
http://www.crunchbase.com/organization/cept-systems
list of suggestions:
1. use cases and implementations for each cortical API call, similar to word2vec use cases and more...
2. webinars:
end-to-end examples of cortical word representation combined with deep learning
end-to-end examples of cortical word representation combined with deep learning deployed on a spark cluster
3. schools, similar to http://www.next.ml/
4. meetup presentations - new tech, hackers and founders, etc.
5, participation in summits - spark, solr, etc...ML, NLP, NLU
6. participation in semeval
7. bloomberg
8. cnbc
9. tests:
How to measure quality of the word vectors
simple test sets that can be used to quickly evaluate the word vector quality -
word relation test set
the phrase relation test
best result, average
example:
comparing apples to apples: a case study using the GloVe modelhttps://docs.google.com/document/d/1ydIujJ7ETSZ688RGfU5IMJJsbxAi-kRl8czSwpti15s/mobilebasic?pli=1
Language processing in the brain
http://en.wikipedia.org/wiki/Language_processing_in_the_brain
Computational Linguistics
http://plato.stanford.edu/entries/computational-linguistics/
SEMEVAL NIST COMPETITIONS
http://alt.qcri.org/semeval2014/
http://alt.qcri.org/semeval2014/task1/index.php?id=results
http://alt.qcri.org/semeval2014/task3/index.php?id=results
http://alt.qcri.org/semeval2014/task5/?id=data-and-tools
word2vec
https://groups.google.com/forum/#!msg/word2vec-toolkit/ZcOst7kEjaI/rv_A6LaE9vkJ
word2vec-toolkit ›
Re: Abridged summary of word2vec...@googlegroups.com - 1 update in 1 topic
1 post by 1 author
Tim Finin
The SemEval workshop (http://en.wikipedia.org/wiki/SemEval) ran tasks in
2012, 2013 and 2014 where the goal was to compute the semantic
similarity of two sentences on a scale from 0 to 5. Each year they
provided training and test datasets with human judgments. These could
easily be used to evaluate and compare the performance of this and other
ideas using word2vec data. Papers on the participating systems can be
found in the ACL repository
(http://aclanthology.info/events/semeval-201+X for X in range(12:15)).
For an overview of the most recent task, see
http://aclanthology.info/papers/semeval-2014-task-10-multilingual-semantic-textual-similarity.
http://www.fi.muni.cz/usr/sojka/papers/pakray-sojka-raslan2014.pdf
An Architecture for Scientific Document Retrieval
Using Textual and Math Entailment Modules
Partha Pakray and Petr Sojka
Faculty of Informatics, Masaryk University
Botanická 68a, 602 00 Brno, Czech Rep
plain Word2vec with pretrained Google news data by LSA gave better result ...Technology (NIST), Evaluation Exercises on Semantic Evaluation (SemEval)5
word2vec entity relationship resolution - as a search key
competing frameworks: stanford nlp vs coreference
http://www.ark.cs.cmu.edu/ARKref/
submissions to the CoNLL 2011 / 2012 shared task on coreference modeling:
http://conll.cemantix.org/2011/http://conll.cemantix.org/2012/
1. was English only, 2012 involved English, Chinese and Arabic.
The Stanford system (Lee et al.'s submission) was the top performing system in 2011, but a few other submissions reported slightly better performance on English in 2012. I'm not sure if any other substantial work has been done on coreference resolution since then.
In my experience, Stanford's system is the winner in usability. Getting a hold of the code for the other submissions can be difficult - your best bet might be to try contacting the authors directly.
Poesio's BART
http://www.bart-coref.org/
The SemEval workshop (http://en.wikipedia.org/wiki/SemEval) ran tasks in
2012, 2013 and 2014 where the goal was to compute the semantic
similarity of two sentences on a scale from 0 to 5. Each year they
provided training and test datasets with human judgments. These could
easily be used to evaluate and compare the performance of this and other
ideas using word2vec data. Papers on the participating systems can be
found in the ACL repository
(http://aclanthology.info/events/semeval-201+X for X in range(12:15)).
For an overview of the most recent task, see
http://aclanthology.info/papers/semeval-2014-task-10-multilingual-semantic-textual-similarity.
What are some standard ways of computing the distance between documents?
There's a number of different ways of going about this depending on exactly how much semantic information you want to retain and how easy your documents are to tokenize (html documents would probably be pretty difficult to tokenize, but you could conceivably do something with tags and context.)
Some of them have been mentioned by ffriend, and the paragraph vectors by user1133029 is a really solid one, but I just figured I would go into some more depth about plusses and minuses of different approaches.
- Cosine Distance - Tried a true, cosine distance is probably the most common distance metric used generically across multiple domains. With that said, there's very little information in cosine distance that can actually be mapped back to anything semantic, which seems to be non-ideal for this situation.
- Levenshtein Distance - Also known as
edit distance, this is usually just used on the individual token level (words, bigrams, etc...). In general I wouldn't recommend this metric as it not only discards any semantic information, but also tends to treat very different word alterations very similarly, but it is an extremely common metric for this kind of thing - LSA - Is a part of a large arsenal of techniques when it comes to evaluating document similarity called
topic modeling. LSA has gone out of fashion pretty recently, and in my experience, it's not quite the strongest topic modeling approach, but it is relatively straightforward to implement and has a few open source implementations - LDA - Is also a technique used for
topic modeling, but it's different fromLSAin that it actually learns internal representations that tend to be more smooth and intuitive. In general, the results you get fromLDAare better for modeling document similarity thanLSA, but not quite as good for learning how to discriminate strongly between topics. - Pachinko Allocation - Is a really neat extension on top of LDA. In general, this is just a significantly improved version of
LDA, with the only downside being that it takes a bit longer to train and open-source implementations are a little harder to come by - word2vec - Google has been working on a series of techniques for intelligently reducing words and documents to more reasonable vectors than the sparse vectors yielded by techniques such as
Count VectorizersandTF-IDF. Word2vec is great because it has a number of open source implementations. Once you have the vector, any other similarity metric (like cosine distance) can be used on top of it with significantly more efficacy. - doc2vec - Also known as
paragraph vectors, this is the latest and greatest in a series of papers by Google, looking into dense vector representations of documents. Thegensimlibrary in python has an implementation ofword2vecthat is straightforward enough that it can pretty reasonably be leveraged to builddoc2vec, but make sure to keep the license in mind if you want to go down this route
http://www.fi.muni.cz/usr/sojka/papers/pakray-sojka-raslan2014.pdf
An Architecture for Scientific Document Retrieval
Using Textual and Math Entailment Modules
Partha Pakray and Petr Sojka
Faculty of Informatics, Masaryk University
Botanická 68a, 602 00 Brno, Czech Rep
plain Word2vec with pretrained Google news data by LSA gave better result ...Technology (NIST), Evaluation Exercises on Semantic Evaluation (SemEval)5
word2vec entity relationship resolution - as a search key
competing frameworks: stanford nlp vs coreference
http://www.ark.cs.cmu.edu/ARKref/
submissions to the CoNLL 2011 / 2012 shared task on coreference modeling:
http://conll.cemantix.org/2011/http://conll.cemantix.org/2012/
1. was English only, 2012 involved English, Chinese and Arabic.
The Stanford system (Lee et al.'s submission) was the top performing system in 2011, but a few other submissions reported slightly better performance on English in 2012. I'm not sure if any other substantial work has been done on coreference resolution since then.
In my experience, Stanford's system is the winner in usability. Getting a hold of the code for the other submissions can be difficult - your best bet might be to try contacting the authors directly.
Poesio's BART
http://www.bart-coref.org/
Tuesday, November 4, 2014
PREDICTIVE ANALYTICS
http://acharyavivek.wordpress.com/2013/02/20/intelligent-bpm-oracle-ibpm/
http://www.oracle.com/us/technologies/bpm/wp-intelligent-bpm-2280473.pdf
Oracle R Enterprise
http://www.oracle.com/technetwork/database/database-technologies/r/r-enterprise/overview/index.htmlOracle R Enterprise, a component of the Oracle Advanced Analytics Option, makes the open source R statistical programming language and environment ready for the enterprise and big data.
Learn More about Oracle R Enterprise
white papers
tutorials
blogs
R Technologies from Oracle
http://www.oracle.com/technetwork/database/database-technologies/r/r-technologies/r-offerings-1566363.htmlhttps://blogs.oracle.com/R/entry/oracle_r_enterprise_tutorial_series
Oracle R Enterprise Tutorial Series on Oracle Learning Library
By Mark Hornick on Oct 02, 2012
Oracle Server Technologies Curriculum has just released the Oracle R Enterprise Tutorial Series, which is publicly available on Oracle Learning Library (OLL). This 8 part interactive lecture series with review sessions covers Oracle R Enterprise 1.1 and an introduction to Oracle R Connector for Hadoop 1.1:
- Introducing Oracle R Enterprise
- Getting Started with ORE
- R Language Basics
- Producing Graphs in R
- The ORE Transparency Layer
- ORE Embedded R Scripts: R Interface
- ORE Embedded R Scripts: SQL Interface
- Using the Oracle R Connector for Hadoop
We encourage you to download Oracle software for evaluation from the Oracle Technology Network. See these links for R-related software: Oracle R Distribution, Oracle R Enterprise, ROracle, Oracle R Connector for Hadoop. As always, we welcome comments and questions on the Oracle R Forum.
Oracle R Enterprise Tutorial Series
https://apex.oracle.com/pls/apex/f?p=44785:24:104767497193176::NO::P24_CONTENT_ID,P24_PREV_PAGE:8984,2
http://www.oracle.com/webfolder/technetwork/tutorials/tutorial/db/ore1.4/part1/presentation.html
http://www.oracle.com/webfolder/technetwork/tutorials/tutorial/db/ore1.4/part2/presentation.html
http://www.inside-r.org/category/packagetags/machinelearning
http://en.wikipedia.org/wiki/Non-negative_matrix_factorization
terminology
Generalized Additive Model residuals
Extract Model Fitted Values
Fitting Generalized Linear Models
Oracle Nimbula:
deviance | up to a constant, minus twice the maximized log-likelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero. |
Oracle Nimbula:
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