Feature Extraction : SPTS and CAPP - computer-vision
I am trying to do facial expression recognition.I want to use Cohn Kanade dataset (The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression). In paper SPTS and CAPP are listed under section FEATURE EXTRACTION. But I can't find any other information on SPTS and CAPP.
What are Full forms of SPTS and CAPP?
From where I can start reading?
The paper is indeed not very good on providing the relevant information in an easily accessible way.
SPTS: "similarity normalized shape features"
CAPP: "Canonical normalized appearance"
CAPP: They cite 3: ( A. Ashraf, S. Lucey, J. Cohn, T. Chen, Z. Ambadar, K. Prkachin,
P. . Solomon, and B.-J. Theobald. The painful face: pain expression
recognition using active appearance models. In Proceedings of the
9th international conference on Multimodal interfaces, pages 9–14,
Nagoya, Aichi, Japan, 2007. ACM. 5)
Related
Gensim: Word2Vec Recommender accuracy Improvement
I am trying to implement something similar in https://arxiv.org/pdf/1603.04259.pdf using awesome gensim library however I am having trouble improving quality of results when I compare to Collaborative Filtering. I have two models one built on Apache Spark and other one using gensim Word2Vec on grouplens 20 million ratings dataset. My apache spark model is hosted on AWS http://sparkmovierecommender.us-east-1.elasticbeanstalk.com and I am running gensim model on my local. However when I compare the results I see superior results with CF model 9 out of 10 times(like below example more similar to searched movie - affinity towards Marvel movies) e.g.:- If I search for Thor movie I get below results Gensim Captain America: The First Avenger (2011) X-Men: First Class (2011) Rise of the Planet of the Apes (2011) Iron Man 2 (2010) X-Men Origins: Wolverine (2009) Green Lantern (2011) Super 8 (2011) Tron:Legacy (2010) Transformers: Dark of the Moon (2011) CF Captain America: The First Avenger Iron Man 2 Thor: The Dark World Iron Man The Avengers X-Men: First Class Iron Man 3 Star Trek Captain America: The Winter Soldier Below is my model configuration, so far I have tried playing with window, min_count and size parameter but not much improvement. word2vec_model = gensim.models.Word2Vec( seed=1, size=100, min_count=50, window=30) word2vec_model.train(movie_list, total_examples=len(movie_list), epochs=10) Any help in this regard is appreciated.
You don't mention what Collaborative Filtering algorithm you're trying, but maybe it's just better than Word2Vec for this purpose. (Word2Vec is not doing awful; why do you expect it to be better?) Alternate meta-parameters might do better. For example, the window is the max-distance between tokens that might affect each other, but the effective windows used in each target-token training randomly chosen from 1 to window, as a way to give nearby tokens more weight. Thus when some training-texts are much larger than the window (as in your example row), some of the correlations will be ignored (or underweighted). Unless ordering is very significant, a giant window (MAX_INT?) might do better, or even a related method where ordering is irrelevant (such as Doc2Vec in pure PV-DBOW dm=0 mode, with every token used as a doc-tag). Depending on how much data you have, your size might be too large or small. Different min_count, negative count, greater 'iter'/'epochs', or sample level might work much better. (And perhaps even things you've already tinkered with would only help after other changes are in place.)
Re-Training spaCy's NER v1.8.2 - Training Volume and Mix of Entity Types
I'm in the process of (re-) training spaCy's Named Entity Recognizer and have a couple of doubts that I hope a more experienced researcher/practitioner can help me figure out:
If a few hundred examples are considered 'a good starting point', then what would be a reasonable number to aim for? Is 100 000 entity/label excessive?
If I introduce a new label, is it best if the number of the entities of that labeled are roughly the same (balanced) during training?
Regarding the mixing in 'examples of other entity types':
do I just add random known categories/labels to my training set eg: ('The Business Standard published in its recent issue on crude oil and natural gas ...', [(4,21, 'ORG')], )?
can I use the same text for various labels? e.g. ('The Business Standard published in its recent issue on crude oil and natural gas ...', [(55,64, 'COMMODITY')], )?
on a similar note let's assume I want spaCyto also recognize a second COMMODITY could I then just use the same sentence and label a different region e.g. ('The Business Standard published in its recent issue on crude oil and natural gas ...', [(69,80, 'COMMODITY')], )? Is that how it's supposed to be done?
what ratio between new and other (old) labels is considered reasonable
Thanks
PS I'm working with Python2.7 in Ubuntu 16.04 using spaCy 1.8.2
For a full answer by Matthew Honnibal check out issue 1054 on spaCy's github page. Below are the most important points as they relate to my questions:
Question(Q) 1: If a few hundred examples are considered 'a good starting point', then what would be a reasonable number to aim for? Is 100 000 entity/label excessive?
Answer(A): Every machine learning problem will have a different examples/accuracy curve. You can get an idea for this by training with less data than you have, and seeing what the curve looks like. If you have 1,000 examples, then try training with 500, 750, etc, and see how that affects your accuracy.
Q 2: If I introduce a new label, is it best if the number of the entities of that label are roughly the same (balanced) during training?
A: There's trade-off between making the gradients too sparse, and making the learning problem too unrepresentative of what the actual examples will look like.
Q 3: Regarding the mixing in 'examples of other entity types':
do I just add random known categories/labels to my training set:
A: No, one should annotate all the entities in that text, so the example above: ('The Business Standard published in its recent issue on crude oil and natural gas ...', [(4,21, 'ORG')], ) should be ('The Business Standard published in its recent issue on crude oil and natural gas ...', [(4,21, 'ORG'), (55,64, 'COMMODITY'), (69,80, 'COMMODITY')], )
can I use the same text for various labels?:
A: Not in the way the examples were given. See previous answer.
what ratio between new and other (old) labels is considered reasonable?:
A: See answer Q 2.
PS: Double citations are direct quotes from the github issue answer.
How to get vector for a sentence from the word2vec of tokens in sentence
I have generated the vectors for a list of tokens from a large document using word2vec. Given a sentence, is it possible to get the vector of the sentence from the vector of the tokens in the sentence.
There are differet methods to get the sentence vectors : Doc2Vec : you can train your dataset using Doc2Vec and then use the sentence vectors. Average of Word2Vec vectors : You can just take the average of all the word vectors in a sentence. This average vector will represent your sentence vector. Average of Word2Vec vectors with TF-IDF : this is one of the best approach which I will recommend. Just take the word vectors and multiply it with their TF-IDF scores. Just take the average and it will represent your sentence vector.
There are several ways to get a vector for a sentence. Each approach has advantages and shortcomings. Choosing one depends on the task you want to perform with your vectors. First, you can simply average the vectors from word2vec. According to Le and Mikolov, this approach performs poorly for sentiment analysis tasks, because it "loses the word order in the same way as the standard bag-of-words models do" and "fail[s] to recognize many sophisticated linguistic phenomena, for instance sarcasm". On the other hand, according to Kenter et al. 2016, "simply averaging word embeddings of all words in a text has proven to be a strong baseline or feature across a multitude of tasks", such as short text similarity tasks. A variant would be to weight word vectors with their TF-IDF to decrease the influence of the most common words. A more sophisticated approach developed by Socher et al. is to combine word vectors in an order given by a parse tree of a sentence, using matrix-vector operations. This method works for sentences sentiment analysis, because it depends on parsing.
It is possible, but not from word2vec. The composition of word vectors in order to obtain higher-level representations for sentences (and further for paragraphs and documents) is a really active research topic. There is not one best solution to do this, it really depends on to what task you want to apply these vectors. You can try concatenation, simple summation, pointwise multiplication, convolution etc. There are several publications on this that you can learn from, but ultimately you just need to experiment and see what fits you best.
It depends on the usage: 1) If you only want to get sentence vector for some known data. Check out paragraph vector in these papers: Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. Eprint Arxiv,4:1188–1196. A. M. Dai, C. Olah, and Q. V. Le. 2015. DocumentEmbedding with Paragraph Vectors. ArXiv e-prints,July. 2) If you want a model to estimate sentence vector for unknown(test) sentences with unsupervised approach: You could check out this paper: Steven Du and Xi Zhang. 2016. Aicyber at SemEval-2016 Task 4: i-vector based sentence representation. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, US 3)Researcher are also looking for the output of certain layer in RNN or LSTM network, recent example is: http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12195 4)For the gensim doc2vec, many researchers could not get good results, to overcome this problem, following paper using doc2vec based on pre-trained word vectors. Jey Han Lau and Timothy Baldwin (2016). An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation. In Proceedings of the 1st Workshop on Representation Learning for NLP, 2016. 5) tweet2vec or sent2vec . Facebook has SentEval project for evaluating the quality of sentence vectors. https://github.com/facebookresearch/SentEval 6) There are more information in the following paper: Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering And for now you can use 'BERT': Google release the source code as well as pretrained models. https://github.com/google-research/bert And here is an example to run bert as a service: https://github.com/hanxiao/bert-as-service
You can get vector representations of sentences during training phase (join the test and train sentences in a single file and run word2vec code obtained from following link). Code for sentence2vec has been shared by Tomas Mikolov here. It assumes first word of a line to be sentence-id. Compile the code using gcc word2vec.c -o word2vec -lm -pthread -O3 -march=native -funroll-loops and run it using ./word2vec -train alldata-id.txt -output vectors.txt -cbow 0 -size 100 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 40 -binary 0 -iter 20 -min-count 1 -sentence-vectors 1 EDIT Gensim (development version) seems to have a method to infer vectors of new sentences. Check out model.infer_vector(NewDocument) method in https://github.com/gojomo/gensim/blob/develop/gensim/models/doc2vec.py
I've had good results from: Summing the word vectors (with tf-idf weighting). This ignores word order, but for many applications is sufficient (especially for short documents) Fastsent
Google's Universal Sentence Encoder embeddings are an updated solution to this problem. It doesn't use Word2vec but results in a competing solution. Here is a walk-through with TFHub and Keras.
Deep averaging network (DAN) can provide sentence embeddings in which word bi-grams are averaged and passed through feedforward deep neural network(DNN). It is found that transfer learning using sentence embeddings tends to outperform word level transfer as it preserves the semantic relationship. You don't need to start the training from scratch, the pretrained DAN models are available for perusal ( Check Universal Sentence Encoder module in google hub).
let suppose this is current sentence
import gensim
from gensim.models import Word2Vec
from gensim import models
model = gensim.models.KeyedVectors.load_word2vec_format('path of your trainig
dataset', binary=True)
strr = 'i am'
strr2 = strr.split()
print(strr2)
model[strr2] //this the the sentance embeddings.
Improving classification results with Weka J48 and Naive Bayes Multinomial classifiers
I have been using Weka’s J48 and Naive Bayes Multinomial (NBM) classifiers upon
frequencies of keywords in RSS feeds to classify the feeds into target
categories.
For example, one of my .arff files contains the following data extracts:
#attribute Keyword_1_nasa_Frequency numeric
#attribute Keyword_2_fish_Frequency numeric
#attribute Keyword_3_kill_Frequency numeric
#attribute Keyword_4_show_Frequency numeric
…
#attribute RSSFeedCategoryDescription {BFE,FCL,F,M, NCA, SNT,S}
#data
0,0,0,34,0,0,0,0,0,40,0,0,0,0,0,0,0,0,0,0,24,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,12,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,10,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,BFE
…
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,FCL
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,F
…
20,0,64,19,0,162,0,0,36,72,179,24,24,47,24,40,0,48,0,0,0,97,24,0,48,205,143,62,7
8,0,0,216,0,36,24,24,0,0,24,0,0,0,0,140,24,0,0,0,0,72,176,0,0,144,48,0,38,0,284,
221,72,0,72,0,SNT
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,SNT
0,0,0,0,0,0,11,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0
,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,S
And so on: there’s a total of 570 rows where each one is contains with the
frequency of a keyword in a feed for a day. In this case, there are 57 feeds for
10 days giving a total of 570 records to be classified. Each keyword is prefixed
with a surrogate number and postfixed with ‘Frequency’.
I am using 10 fold x validation for both the J48s and NBM classifiers on a
'black box' basis. Other parameters used are also defaults, i.e. 0.25 confidence
and min number of objects is 2 for the J48s.
So far, my classification rates for an instance of varying numbers of days, date
ranges and actual keyword frequencies with both J28 and NBM results being
consistent in the 50 - 60% range. But, I would like to improve this if possible.
I have reduced the decision tree confidence level, sometimes as low as 0.1 but
the improvements are very marginal.
Can anyone suggest any other way of improving my results?
To give more information, the basic process here involves a diverse collection of RSS feeds where each one belongs to a single category.
For a given date range, e.g. 01 - 10 Sep 2011, the text of each feed's item elements are combined. The text is then validated to remove words with numbers, accents and so on, and stop words (a list of 500 stop words from MySQL is used). The remaining text is then indexed in Lucene to work out the most popular 64 words.
Each of these 64 words is then searched for in the description elements of the feeds for each day within the given date range. As part of this, the description text is also validated in the same way as the title text and again indexed by Lucene. So a popular keyword from the title such as 'declines' is stemmed to 'declin': then if any similar words are found in the description elements which also stem to 'declin', such as 'declined', the frequency for 'declin' is taken from Lucene's indexing of the word from the description elements.
The frequencies shown in the .arff file match on this basis, i.e. on the first line above, 'nasa', 'fish', 'kill' are not found in the description items of a particular feed in the BFE category for that day, but 'show' is found 34 times. Each line represents occurrences in the description items of a feed for a day for all 64 keywords.
So I think that the low frequencies are not due to stemming. Rather I see it as the inevitable result of some keywords being popular in feeds of one category, but which don't appear in other feeds at all. Hence the spareness shown in the results. Generic keywords may also be pertinent here as well.
The other possibilities are differences in the numbers of feeds per category where more feeds are in categories like NCA than S, or the keyword selection process itself is at fault.
You don't mention anything about stemming. In my opinion you could have better results if you were performing word stemming and the WEKA evaluation was based on the keyword stems. For example let's suppose that your WEKA model is built given a keyword surfing and a new rss feed contains the word surf. There should be a match between these two words. There are many free available stemmers for several languages. For the English language some available options for stemming are: The Porter's stemmer Stemming based on the WordNet's dictionary In case you would like to perform stemming using the WordNet's dictionary, there are libraries & frameworks that perform integration with WordNet. Below you can find some of them: MIT Java WordNet interface (JWI) Rita Java WorNet Library (JWNL) EDITED after more information was provided I believe that the keypoint in the specified case is the selection of the "most popular 64 words". The selected words or phrases should be keywords or keyphrases. So the challenge here is the keywords or keyphrases extraction. There are several books, papers and algorithms written about keywords/keyphrases extraction. The university of Waikato has implemented in JAVA, a famous algorithm called Keyword Extraction Algorithm (KEA). KEA extracts keyphrases from text documents and can be either used for free indexing or for indexing with a controlled vocabulary. The implementation is distributed under the GNU General Public License. Another issue that should be taken into consideration is the (Part of Speech)POS tagging. Nouns contain more information than the other POS tags. Therefore may you would have better results if you were checking the POS tag and the selected 64 words were mostly nouns. In addition according to the Anette Hulth's published paper Improved Automatic Keyword Extraction Given More Linguistic Knowledge, her experiments showed that the keywords/keyphrases mostly have or are contained in one of the following five patterns: ADJECTIVE NOUN (singular or mass) NOUN NOUN (both sing. or mass) ADJECTIVE NOUN (plural) NOUN (sing. or mass) NOUN (pl.) NOUN (sing. or mass) In conclusion a simple action that in my opinion could improve your results is to find the POS tag for each word and select mostly nouns in order to evaluate the new RSS feeds. You can use WordNet in order to find the POS tag for each word and as I mentioned above there are many libraries on the web that perform integration with the WordNet's dictionary. Of course stemming is also essential for the classification process and has to be maintained. I hope this helps.
Try turning off stemming altogether. The Stanford Intro to IR authors provide a rough justification of why stemming hurts, and at the very least does not help, in text classification contexts. I have tested stemming myself on a custom multinomial naive Bayes text classification tool (I get accuracies of 85%). I tried the 3 Lucene stemmers available from org.apache.lucene.analysis.en version 4.4.0, which are EnglishMinimalStemFilter, KStemFilter and PorterStemFilter, plus no stemming, and I did the tests on small and larger training document corpora. Stemming significantly degraded classification accuracy when the training corpus was small, and left accuracy unchanged for the larger corpus, which is consistent with the Intro to IR statements. Some more things to try: Why only 64 words? I would increase that number by a lot, but preferably you would not have a limit at all. Try tf-idf (term frequency, inverse document frequency). What you're using now is just tf. If you multiply this by idf you can mitigate problems arising from common and uninformative words like "show". This is especially important given that you're using so few top words. Increase the size of the training corpus. Try shingling to bi-grams, tri-grams, etc, and combinations of different N-grams (you're now using just unigrams). There's a bunch of other knobs you could turn, but I would start with these. You should be able to do a lot better than 60%. 80% to 90% or better is common.
What is the best data mining method for vehicle search?
I'm trying to build a search engine that goes through online vehicle classifieds such as Oodle, eBay motors, and craigslist. I also have a large database of standard vehicle names and specifications about them. What I would like to do is for each record that I find through the classified site, be able to determine exactly what vehicle model, style it is (from my database). For example, a standard name for a ford truck in my db is: 2003 Ford F150. However on classified sites, people might refer to is as: "2003 Ford F 150" or "2003 Ford f-150" or "03 Ford truck 150". Is there an effective data mining/text classification algorithm to be able to normalize these texts to the standard name above?
You could use the Levenshtein distance to match the found string against your database records. Another (probably better) idea is to tokenize the strings and use a term vector model for the vehicle names. This way you can use cosine similarity to find relevant matches.
If you're gonna develop a whole search engine intended to scale in both, usage and size, you will need something robust to support your queries. If you're gonna used edit distance, Bed-trees provide a good alternative for your index structure. Another good approach, depending on the size of your dataset, is to use a Levenshtein automata. Levenshtein automatas are also great at providing auto-complete functionalities, which you may need since you're developing a search engine. Another approach to edit distance is to use n-grams combined with Jaccard index. For this approach you can use Minhash + LSH. Also, you can use Jaccard as a distance metric (1 - Jaccard index) which respects the triangle inequality, thus, can be used in a metric tree such as a VP-tree. One of these approaches will certainly help you.