Develop Reference

improve gensim most_similar() return values by using wordnet hypernyms

import gensim.downloader as api
glove = api.load('glove-wiki-gigaword-200')
I first ran this code to download the pre-trained model.
glove.most_similar(positive=['sushi', 'uae'], negative=['japan'])
would then result in:
[('nahyan', 0.5181387066841125),
('caviar', 0.4778318405151367),
('paella', 0.4497394263744354),
('nahayan', 0.44313961267471313),
('zayed', 0.4321245849132538),
('omani', 0.4285220503807068),
('seafood', 0.4279175102710724),
('saif', 0.426000714302063),
('dirham', 0.4214130640029907),
('sashimi', 0.4165934920310974)]
and in this example, we can see that the method failed to capture the 'type' or 'category' of the query. 'zayed', 'nahyan' are not actually of 'type' food and rather they represent person name.
The approach suggested by my professor is to use wordnet hypernyms to find the 'type'.
With much research, the closest solution I found is to somehow incorporate
lowest_common_hypernyms() that will give the lowest common hypernym between two synsets and use it to filter the results of most_similar().
I am not sure if my idea make sense and would like the community feedback on this.
My idea is compute the hypernym of, e.g. 'sushi' and the hypernyms of all the similar words returned by most_similar() and only choose the word with 'longest' lowest common hypernym path. I expect this should return the word that best matches the 'type'
Not sure if it makes sense...

Does your proposed approach give adequate results when you try it?
That's the only test of whether the idea makes sense.
Word2vec is generally oblivious to the all the variations of category that a lexicon like WordNet can provide – all the words that are similar to another word, in any aspect, will be neighbors. Even words that people consider opposites – like 'hot' and 'cold' – will be often be fairly close to each other, in some direction in the coordinate space, as they are similar in what they describe and what contexts they're used in. (They can be drop-in replacements for each other.)
Word2vec is also fairly oblivious to polysemy in its standard formulation.
Some other things worth trying might be:
if you need only answers of a certain type, mix-in some measurement ranking candidate answers by their closeness to a word either describing that type ('food') or representing multiple examples (say an average vector for many food-names you'd know to be good answers)
choose another vector-set, or train your own. There's no universal "goodness" for word-vectors: their quality for certain tasks will vary based on their training data & parameters. Vectors trained on something broader than Wikipedia (your named vector file), or some text corpus more focused on your domain-of-interest – say, food criticism – might do better on some tasks. Changing training parameters can also change which kinds of similarity are most emphasized in the resulting vectors. For example, some observers have noticed small context-windows tend to put words that are direct drop-in replacements for each other closer-together, while larger context-windows bring words from the same domains-of-use, even if not drop-in replacements of the same 'type', closer. (It sounds like your current need might be best served with a model trained with smaller windows.)

Nahyan is from the UAE - it seems to be part of the name of all three presidents. So you seem to be getting what you ask for. If you want more foods, add "food" to your positive query, and maybe "people" to your negative query?
Another approach is to post-filter your results to remove anything that isn't a food. Or is a person. (WordNet won't be much help, as it is nowhere near comprehensive on foods, and even less so on people; Wikidata is likely to be more useful.)
By the way, if you find the common hypernym of sushi and UAE it will probably be the top-level entity in wordnet. So that will give you no filtering.

Use word2vec to expand a glossary in order to classify texts

I have a database containing about 3 million texts (tweets). I put clean texts (removing stop words, tags...) in a list of lists of tokens called sentences (so it contains a list of tokens for each text).
After these steps, if I write
model = Word2Vec(sentences, min_count=1)
I obtain a vocabulary of about 400,000 words.
I have also a list of words (belonging to the same topic, in this case: economics) called terms. I found that 7% of the texts contain at least one of these words (so we can say that 7% of total tweets talk about economics).
My goal is to expand the list terms in order to retrieve more texts belonging to the economic topic.
Then I use
results = model.most_similar(terms, topn=5000)
to find, within the list of lists of tokens sentences, the words most similar to those contained in terms.
Finally if I create the data frame
df = pd.DataFrame(results, columns=['key', 'similarity'])
I get something like that:
key similarity
word1 0.795432
word2 0.787954
word3 0.778942
... ...
Now I think I have two possibilities to define the expanded glossary:
I take the first N words (what should be the value of N?);
I look at the suggested words one by one and decide which one to include in the expanded glossary based on my knowledge (does this word really belong to the economic glossary?)
How should I proceed in a case like this?

There's no general answer for what the cutoff should be, or how much you should use your own manual judgement versus cruder (but fast/automatic) processes. Those are inherently decisions which will be heavily influenced by your data, model quality, & goals – so you have to try different approaches & see what works there.
If you had a goal for what percentage of the original corpus you want to take – say, 14% instead of 7% – you could go as deeply into the ranked candidate list of 'similar words' as necessary to hit that 14% target.
Note that when you retrieve model.most_similar(terms), you are asking the model to 1st average all words in terms together, then return words close to that one average point. To the extent your seed set of terms is tightly around the idea of economics, that might find words close to that generic average idea – but might not find other interesting words, such as close sysnonyms of your seed words that you just hadn't thought of. For that, you might want to get not 5000 neighbors for one generic average point, but (say) 3 neighbors for every individual term. To the extent the 'shape' of the topic isn't a perfect sphere around someplace in the word-vector-space, but rather some lumpy complex volume, that might better reflect your intent.
Instead of using your judgement of the candidate words standing alone to decide whether a word is economics-related, you could instead look at the texts that a word uniquely brings in. That is, for new word X, look at the N texts that contain that word. How many, when applying your full judgement to their full text, deserve to be in your 'economics' subset? Only if it's above some threshold T would you want to move X into your glossary.
But such an exercise may just highlight: using a simple glossary – "for any of these hand-picked N words, every text mentioning at least 1 word is in" – is a fairly crude way of assessing a text's topic. There are other ways to approach the goal of "pick a relevant subset" in an automated way.
For example, you could view your task as that of training a text binary classifier to classify texts as 'economics' or 'not-economics'.
In such a case, you'd start with some training data - a set of example documents that are already labeled 'economics' or 'not-economics', perhaps via individual manual review, or perhaps via some crude bootstrapping (like labeling all texts with some set of glossary words as 'economics', & all others 'not-economics'). Then you'd draw from the full range of potential text-preprocessing, text-feature-extracton, & classification options to train & evaluate classifiers that make that judgement for you. Then you'd evaluate/tune those – a process wich might also improve your training data, as you add new definitively 'economics' or 'not-economics' texts – & eventually settle on one that works well.
Alternatively, you could use some other richer topic-modeling methods (LDA, word2vec-derived Doc2Vec, deeper neural models etc) for modeling the whole dataset, then from some seed-set of definite-'economics' texts, expand outward from them – finding nearest-examples to known-good documents, either auto-including them or hand-reviewing them.
Separately: min_count=1 is almost always a mistake in word2vec & related algorihtms, which do better if you discard words so rare they lack the variety of multiple usage examples the algorithm needs to generate good word-vectors.

What should be used between Doc2Vec and Word2Vec when analyzing product reviews?

I collected some product reviews of a website from different users, and I'm trying to find similarities between products through the use of the embeddings of the words used by the users.
I grouped each review per product, such that I can have different reviews succeeding one after the other in my dataframe (i.e: different authors for one product). Furthermore, I also already tokenized the reviews (and all other pre-processing methods). Below is a mock-up dataframe of what I'm having (the list of tokens per product is actually very high, as well as the number of products):
Product
reviews_tokenized
XGame3000
absolutely amazing simulator feel inaccessible ...
Poliamo
production value effect tend cover rather ...
Artemis
absolutely fantastic possibly good oil ...
Ratoiin
ability simulate emergency operator town ...
However, I'm not sure of what would be the most efficient between doc2Vec and Word2Vec. I would initially go for Doc2Vec, since it has the ability to find similarities by taking into account the paragraph/sentence, and find the topic of it (which I'd like to have, since I'm trying to cluster products by topics), but I'm a bit worry about the fact that the reviews are from different authors, and thus might bias the embeddings? Note that I'm quite new to NLP and embeddings, so some notions may escape me. Below is my code for Doc2Vec, which giving me a quite good silhouette score (~0.7).
product_doc = [TaggedDocument(doc.split(' '), [i]) for i, doc in enumerate(df.tokens)]
model3 = Doc2Vec(min_count=1, seed = SEED, ns_exponent = 0.5)
model3.build_vocab(product_doc)
model3.train(product_doc, total_examples=model3.corpus_count, epochs=model3.epochs)
product2vec = [model3.infer_vector((df['tokens'][i].split(' '))) for i in range(0,len(df['tokens']))]
dtv = np.array(product2vec)
What do you think would be the most efficient method to tackle this? If something is not clear enough, or else, please tell me.
Thank you for your help.
EDIT: Below is the clusters I'm obtaining:

There's no way to tell which particular mix of methods will work best for a specific dataset and particular end-goal: you really have to try them against each other, in your own reusable pipeline for scoring them against your desired results.
It looks like you've already stripped the documents down to keywords rather than original natural text, which could hurt with these algorithms - you may want to try it both ways.
Depending on the size & format of your texts, you may also want to look at doing "Word Mover's Distance" (WMD) comparisons between sentences (or other small logical chunks of your data). Some work has demo'd interesting results in finding "similar concerns" (even with different wording) in the review domain, eg: https://tech.opentable.com/2015/08/11/navigating-themes-in-restaurant-reviews-with-word-movers-distance/
Note, though, WMD gets quite costly to calculate in bulk with larger texts.

How to generate homophones on substring level?

I want to generate homophones of words programmatically. Meaning, words that sound similar to the original words.
I've come across the Soundex algorithm, but it just replaces some characters with other characters (like t instead of d). Are there any lists or algorithms that are a little bit more sophisticated, providing at least homophone substrings?
Important: I want to apply this on words that aren't in dictionaries, meaning that I can't rely on whole, real words.
EDIT:
The input is a string which is often a proper name and therefore in no standard (homophone) dictionary. An example could be Google or McDonald's (just to name two popular named entities, but many are much more unpopular).
The output is then a (random) homophone of this string. Since words often have more than one homophone, a single (random) one is my goal. In the case of Google, a homophone could be gugel, or MacDonald's for McDonald's.

How to do this well is a research topic. See for example http://www.inf.ufpr.br/didonet/articles/2014_FPSS.pdf.
But suppose that you want to roll your own.
The first step is figuring out how to turn the letters that you are given into a representation of what it sounds like. This is a very hard problem with guessing required. (eg What sound does "read" make? Depends on whether you are going to read, or you already read!) However text to phonemes converter suggests that Arabet has solved this for English.
Next you'll want this to have been done for every word in a dictionary. Assuming that you can do that for one word, that's just a script.
Then you'll want it stored in a data structure where you can easily find similar sounds. That is in principle no difference than the sort of algorithms that are used for autocorrect for spelling. Only with phonemes instead of letters. You can get a sense of how to do that with http://norvig.com/spell-correct.html. Or try to implement something like what is described in http://fastss.csg.uzh.ch/ifi-2007.02.pdf.
And that is it.

How to automatically label a cluster of words using semantics?

The context is : I already have clusters of words (phrases actually) resulting from kmeans applied to internet search queries and using common urls in the results of the search engine as a distance (co-occurrence of urls rather than words if I simplify a lot).
I would like to automatically label the clusters using semantics, in other words I'd like to extract the main concept surrounding a group of phrases considered together.
For example - sorry for the subject of my example - if I have the following bunch of queries : ['my husband attacked me','he was arrested by the police','the trial is still going on','my husband can go to jail for harrassing me ?','free lawyer']
My study deals with domestic violence, but clearly this cluster is focused on the legal aspect of the problem so the label could be "legal" for example.
I am new to NPL but I have to precise that I don't want to extract words using POS tagging (or at least this is not the expected final outcome but maybe a necessary preliminary step).
I read about Wordnet for sense desambiguation and I think that might be a good track, but I don't want to calculate similarity between two queries (since the clusters are the input) nor obtain the definition of one selected word thanks to the context provided by the whole bunch of words (which word to select in this case ?). I want to use the whole bunch of words to provide a context (maybe using synsets or categorization with the xml structure of the wordnet) and then summarize the context in one or few words.
Any ideas ? I can use R or python, I read a little about nltk but I don't find a way to use it in my context.

Your best bet is probably is to label the clusters manually, especially if there are few of them. This a difficult problem even for humans to solve, because you might need a domain expert. Anyone claiming they could do that automatically and reliably (except in some very limited domains) is probably running a startup and trying to get your business.
Also, going through the clusters yourself will have benefits. 1) you may discover you had the wrong number of clusters (k parameter) or that there was too much junk in the input to begin with. 2) you will gain qualitative insight into what is being talked about and what topic there are in the data (which you probably can't know before looking at the data). Therefore, label manually if qualitative insight is what you are after. If you need quantitative result too, you could then train a classifier on the manually labelled topics to 1) predict topics for the rest of the clusters, or 2) for future use, if you repeat the clustering, get new data, ...

When we talk about semantics in this area we mean Statistical Semantics. The statistical or distributional semantics is very different from other definitions of semantics which has logic and reasoning behind it. Statistical semantics is based on Distributional Hypothesis, which considers context as meaning aspect of words and phrases. Meaning in very abstract and general sense in different litterers is called topics. There are several unsupervised methods for modelling topics, such as LDA or even word2vec, which basically provide word similarity metric or suggest a list of similar words for a document as another context. Usually when you have these unsupervised clusters, you need a domain expert to tell the meaning of each cluster.
However, for several reasons you might accept low accuracy assignment of a word as the general topic (or as in your words "global semantic") to a list of phrases. If this is the case, I would suggest to take a look at Word Sense Disambiguation tasks which look for coarse grained word senses. For WordNet, it might be called supersense tagging task.
This paper worth to take a look: More or less supervised supersense tagging of Twitter
And about your question about choosing words from current phrases, there is also an active question about "converting phrase to vectors", my answer to that question in word2vec fashion might be useful:
How can a sentence or a document be converted to a vector?
I can add more related papers later if it comes to my mind.

The paper Automatic Labelling of Topic Models explains the author's approach to this problem. To provide an overview I can tell you that they generate some label candidates using the information retrieved from Wikipedia and Google, and once they have the list of candidates in place they rank those candidates to find the best label.
I think the code is not available online, but I have not looked for it.

The package chowmein claims to do this in python using the algorithm outlined in Automatic Labeling of Multinomial Topic Models.

One possible approach, which the below papers suggest is identifying the set of keywords from the cluster, getting all the synonyms and then finding the hypernyms for each synonym.
The idea is to get a more abstract meaning for the cluster by using the hypernym.
Example: A word cluster containing words dog and wolf should not be labelled with either word but as canids. They achieve it using synonymy and hypernymy.
Cluster Labeling by Word Embeddings
and WordNet’s Hypernymy
Automated Text Clustering and Labeling using Hypernyms