Clear approach for assigning semantic tags to each sentence (or short documents) in python - python-2.7
I am looking for a good approach using python libraries to tackle the following problem:
I have a dataset with a column that has product description. The values in this column can be very messy and would have a lot of other words that are not related to the product. I want to know which rows are about the same product, so I would need to tag each description sentence with its main topics. For example, if I have the following:
"500 units shoe green sport tennis import oversea plastic", I would like the tags to be something like: "shoe", "sport". So I am looking to build an approach for semantic tagging of sentences, not part of speech tagging. Assume I don't have labeled (tagged) data for training.
Any help would be appreciated.
Lack of labeled data means you cannot apply any semantic classification method using word vectors, which would be the optimal solution to your problem. An alternative however could be to construct the document frequencies of your token n-grams and assume importance based on some smoothed variant of idf (i.e. words that tend to appear often in descriptions probably carry some semantic weight). You can then inspect your sorted-by-idf list of words and handpick(/erase) words that you deem important(/unimportant). The results won't be perfect, but it's a clean and simple solution given your lack of training data.
Related
Vector embeddings to mimic a ranking algorithm
Consider a search system where the user submits a query ‘query’ and retrieves products based on some ranking algorithm. Assume that these products are ordered according to their quality such that p_0, p_1, …, p_10 and so on. I would like to generate vector embeddings that mimic this ranking algorithm. The closest product vector to a query vector should ideally be p_0, the next one should be p_1 and so on. I have tried to building word2vec embeddings for products by feeding products that have appeared in the same search session as sentences. Then, I have calculated the weighted average of product vectors to find query vectors to make the query vector closer to the top result. Although the closest result is usually the best result for a given query, the subsequent results include some results that would never appear as a top result. Is there a trick that the word2vec can learn the ranking algorithm or any other techniques that I can try? I have looked into multi-dimensional vector scaling with non-metric distances but it did not seem scalable to me for more than 100Ks of products.
There's no one trick – just iteratively improving your representations, & training set, & ranking methods to better meet your goals. Word2vec-based representations can often help, but are still fairly simple & centered on individual words – whose senses may vary based on context & position in ways that a simple weighted-average-of-tokens fails to capture. You may want to represent 'products' by more than just a string-of-word-tokens – to include other properties, as well. These could be scalar values like prices or various other kinds of ratings/properties, or extra synthetic labels, such as the result of other salient groupings (whether hand-edited or learned). And even if just working with natural-language product descriptions – like product names, or descriptions, or reviews – there are other more-sophisticated text-representations that can be trained or used – such as sentence/document embeddings using deeper-networks than plain word2vec. Most generically, if you have a bunch of quantitative representations of candidate results, and a query, and want to use some initial examples of "good" results to bootstrap more generalizable rules for scoring top results, you are attempting a "learning-to-rank" process: https://en.wikipedia.org/wiki/Learning_to_rank To suggest more specific steps would require a more specific description of inputs/outputs/goals, & what's been tried, and how what's been tried has failed. For example, are your queries always just textual product names? In such a case, maybe plain keyword search is the central technology required – with things like word-vector-modelling just a tweak for handling some tough cases, like expanding the results, or adding more contrast to the rankings, when results are too few or to many. Or, can you detect key gaps in the modeling related to exactly those cases where "results include some results that would [ideally] never appear as a top result"? If certain things like rare (poorly-modeled) words, or important qualities not yet captured in the model, seem to be to blame for such cases, that will guide the potential set of corrective changes.
Document classification: Preprocessing and multiple labels
I have a question about the word representation algorithms: Which one of the algorithms word2Vec, doc2Vec and Tf-IDF is more suitable for handling text classification tasks ? The corpus used in my supervised learning classification is composed of a list of multiple sentences, with both short length sentences and long length ones. As discussed in this thread, doc2vec vs word2vec choice is a matter of document length. As for Tf-Idf vs. word embedding, it's more a matter of text representation. My other question is, what if for the same corpus I had more than one label to link to the sentences in it ? If I create multiple entries/labels for the same sentence, it affects the decision of the final classification algorithm. How can I tell the model that every label counts equal for every sentence of the document ? Thank you in advance,
You should try multiple methods of turning your sentences into 'feature vectors'. There are no hard-and-fast rules; what works best for your project will depend a lot on your specific data, problem-domains, & classification goals. (Don't extrapolate guidelines from other answers – such as the one you've linked that's about document-similarity rather than classification – as best practices for your project.) To get initially underway, you may want to focus on some simple 'binary classification' aspect of your data, first. For example, pick a single label. Train on all the texts, merely trying to predict if that one label applies or not. When you have that working, so you have a understanding of each step – corpus prep, text processing, feature-vectorization, classification-training, classification-evaluation – then you can try extending/adapting those steps to either single-label classification (where each text should have exactly one unique label) or multi-label classification (where each text might have any number of combined labels).
calculate nearest document using fasttext or word2vec
i have a small system of about 1000 documents. For each document I would like to show links to the X "most similar" documents. However, the documents are not labeled in any way, so this would be some kind of unsupervised method. It feels like fasttext would be a good candidate, but I cant wrap my head around how to do it when its not labeled data. I can calculate the word vectors, although what I really need is a vector for the whole document.
The Paragraph Vector algorithm, known as Doc2Vec in libraries like Python gensim, can train a model that will give a single vector for a run-of-text, and so could be useful for your need. Note, though, that typical published work uses tens-of-thousands to millions of documents. (Just 1,000 would be a very small training set.) You can also simply average all the word-vectors of a text together (perhaps in some weighted fashion) to get a simple, crude vector for the full text, that will often somewhat work for this purpose. (You could use word-vectors from classi word2vec or FastText for this purpose.) Similarly, if you have word-vectors but not full doc-vectors, there's a technique called "Word Mover's Distance" that calculates a word-vector-adjusted "distance" between two texts. It often does well in highlighting near-paraphrases, though it's somewhat expensive to calculate (especially for longer texts). In some cases, just converting all docs to their "bag of words" representation – a giant vector containing counts of words used – then ranking docs by how many words they share is a good enough similarity measure. Also, full-text index/search frameworks, like SOLR or ElasticSearch, can sometimes take full documents as queries, giving nicly ranked results. (This often works by picking the example document's most significant words, and using those words as fuzzy full-text queries against the full document set.)
word2vec guesing word embeddings
can word2vec be used for guessing words with just context? having trained the model with a large data set e.g. Google news how can I use word2vec to predict a similar word with only context e.g. with input ", who dominated chess for more than 15 years, will compete against nine top players in St Louis, Missouri." The output should be Kasparov or maybe Carlsen. I'ven seen only the similarity apis but I can't make sense how to use them for this? is this not how word2vec was intented to use?
It is not the intended use of word2vec. The word2vec algorithm internally tries to predict exact words, using surrounding words, as a roundabout way to learn useful vectors for those surrounding words. But even so, it's not forming exact predictions during training. It's just looking at a single narrow training example – context words and target word – and performing a very simple comparison and internal nudge to make its conformance to that one example slightly better. Over time, that self-adjusts towards useful vectors – even if the predictions remain of wildly-varying quality. Most word2vec libraries don't offer a direct interface for showing ranked predictions, given context words. The Python gensim library, for the last few versions (as of current version 2.2.0 in July 2017), has offered a predict_output_word() method that roughly shows what the model would predict, given context-words, for some training modes. See: https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.predict_output_word However, considering your fill-in-the-blank query (also called a 'cloze deletion' in related education or machine-learning contexts): _____, who dominated chess for more than 15 years, will compete against nine top players in St Louis, Missouri A vanilla word2vec model is unlikely to get that right. It has little sense of the relative importance of words (except when some words are more narrowly predictive of others). It has no sense of grammar/ordering, or or of the compositional-meaning of connected-phrases (like 'dominated chess' as opposed to the separate words 'dominated' and 'chess'). Even though words describing the same sorts of things are usually near each other, it doesn't know categories to be able to determine that the blank must be a 'person' and a 'chess player', and the fuzzy-similarities of word2vec don't guarantee words-of-a-class will necessarily all be nearer-each-other than other words. There has been a bunch of work to train word/concept vectors (aka 'dense embeddings') to be better at helping at such question-answering tasks. A random example might be "Creating Causal Embeddings for Question Answering with Minimal Supervision" but queries like [word2vec question answering] or [embeddings for question answering] will find lots more. I don't know of easy out-of-the-box libraries for doing this, with or without a core of word2vec, though.
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.