I have a noob question, go easy on me — I'll probably get the terminology wrong. I'm hoping someone can give me the "here's what to google next" explanation for how to approach creating a CoreML model that can identify tokens within spans. Since my question falls between the hello world examples and the intellectual papers that cover the topics in detail, it has been hard to google for.
I'm taking my first stab at doing some natural language processing, specifically parsing data out of recipe ingredients. CreateML supports word tagging, which I interpret to mean Named Entity Recognition — split a string into tokens (probably words), annotate them, feed them to the model.
"1 tablespoon (0.5 oz / 14 g) baking soda"
This scenario immediately breaks my understanding of word tagging. Tokenize this by words, this includes three measurements. However, this is really one measurement with a clarification that contains two alternate measurements. What I really want to do is to label "(0.5 oz / 14 g)" as a clarification which contains measurements.
Or how about "Olive oil". If I were tokenizing by words, I'd probably get two tokens labeled as "ingredient" which I'd interpret to mean I have two ingredients, but they go together as one.
I've been looking at https://prodi.gy/ which does span categorization, and seemingly handles this scenario — tokenize, then name the entities, then categorize them into spans. However, as far as I understand it, spans are an entirely different paradigm which wouldn't convert over to CoreML.
My naive guess for how you'd do this in CoreML is that I use multiple models, or something that works recursively — one pass would tokenize "(0.5 oz / 14 g)" as a single token labeled as "clarification" and then the next pass would tokenize it into words. However, this smells like a bad idea.
So, how does one solve this problem with CoreML? Code is fine, if relevant, but I'm really just asking about how to think about the problem so I can continue my research.
Thanks for your help!
Related
I need some advice on the following problem.
I'm given a set of weighted keywords (by percentage) and need to find a text in a database that best matches those keywords. I will give an example.
I'm presented with these keywords
Sun(90%)
National Park(85% some keywords contain 2 words)
Landmark(60%)
Now lets say my database contains 3 entries of texts e.g
Going-to-the-Sun Road is a scenic mountain road in the Rocky Mountains of the western United States, in Glacier National Park in Montana.
Everybody has a little bit of the sun and moon in them. Everybody has a little bit of man, woman, and animal in them.
A hybrid car is one that uses more than one means of propulsion - that means combining a petrol or diesel engine with an electric motor.
Obviously the first text is the one that best describes the given set of keywords so this is what I want to recommend to the user. Following the second text that somewhat relates with the "sun" keyword and that could be an acceptable choice too.
The 3rd text is totally irrelevant and shall only be recommended as a last resort when everything else fails.
I'm totally new to that kind of stuff so I need some advice as to which technologies/algorithms I should use. Seems like there is some machine learning (nlp) involved or some kind of fuzzy logic. I'm not really sure.
You need to use a combination of query terms boosting and synonyms
Look into Is there a way to do fuzzy string matching for words on string?
My Motivations I'm trying to learn German and realized there's a confounding fact with the structure of German: every noun has a gender which seems unrelated to the noun itself in many cases.
Unlike languages such as English, each noun has a different definite article, depending on gender: der (masculine), die (feminine), and das (neuter). For example:
das Mädchen ("the girl"), der Rock ("the skirt), die Hose ("the trousers/pants"). So, there seems to be no correlation between gender assignment of nouns and their meanings.
The Data
I gathered up to 5000 German words with 3 columns (das, der, die) for each word with 1's and 0's. So, my data is already clustered with one hot encoding and I'm not trying to predict anything.
Why I'm here I am clueless on where to start, how to approach this problem as the concept of distance in clustering doesn't make sense to me in this setting. I can't think of a way to generate an understandable description of these clusters. The mixed data makes it impossible for me to think of some hard-coded metrics for evaluation.
So, my question is:
I want to find some patterns, some characteristics of these words that made them fall in a specific cluster. I don't know if I'm making any sense but some people managed to find some patterns already (for example word endings, elongated long objects tend to be masculine etc., etc) and I believe ML/AI could do a way better job at this. Would it be possible for me to do something like this?
Some personal thoughts
While I was doing some research (perhaps, naive), I realized the potential options are decision trees and cobweb algorithms. Also, I was thinking if I could just scrape a few images (say 5) for every word and try to run some image classification and see the intermediate NN's to see if any specific shapes support a specific object gender. In addition to that, I was wondering whether scraping the data of google n-gram viewers of these words could help in anyway. I couldn't think of a way to use NLP or its sub domains.
Alternatives If everything I just wrote sounds nonsensical, please suggest me a way to make visual representations of my dataframe (more like nodes and paths with images at nodes, one for each cluster) in Python so that I could make pictorial mind maps and try to by heart them.
The ultimate purpose is to make learning German simpler for myself and possibly for others
I am searching for information on algorithms to process text sentences or to follow a structure when creating sentences that are valid in a normal human language such as English. I would like to know if there are projects working in this field that I can go learn from or start using.
For example, if I gave a program a noun, provided it with a thesaurus (for related words) and part-of-speech (so it understood where each word belonged in a sentence) - could it create a random, valid sentence?
I'm sure there are many sub-sections of this kind of research so any leads into this would be great.
The field you're looking for is called natural language generation, a subfield of natural language processing
http://en.wikipedia.org/wiki/Natural_language_processing
Sentence generation is either really easy or really hard depending on how good you want the sentences to be. Currently, there aren't programs that will be able to generate 100% sensible sentences about given nouns (even with a thesaurus) -- if that is what you mean.
If, on the other hand, you would be satisfied with nonsense that was sometimes ungrammatical, then you could try an n-gram based sentence generator. These just chain together of words that tend to appear in sequence, and 3-4-gram generators look quite okay sometimes (although you'll recognize them as what generates a lot of spam email).
Here's an intro to the basics of n-gram based generation, using NLTK:
http://www.nltk.org/book/ch02.html#generating-random-text-with-bigrams
This is called NLG (Natural Language Generation), although that is mainly the task of generating text that describes a set of data. There is also a lot of research on completely random sentence generation as well.
One starting point is to use Markov chains to generate sentences. How this is done is that you have a transition matrix that says how likely it is to transition between every every part-of-speech. You also have the most likely starting and ending part-of-speech of a sentence. Put this all together and you can generate likely sequences of parts-of-speech.
Now, you are far from done, this will first of all not offer a very good result as you are only considering the probability between adjacent words (also called bi-grams), so what you want to do is to extend this to look for instance at the transition matrix between three parts-of-speech (this makes a 3D matrix and gives you trigrams). You can extend it to 4-grams, 5-grams, etc. depending on the processing power and if your corpus can fill such matrix.
Lastly, you need to patch up things such as object agreement (subject-verb-agreement, adjective-verb-agreement (not in English though), etc.) and tense, so that everything is congruent.
Yes. There is some work dealing with solving problems in NLG with AI techniques. As far as I know, currently, there is no method that you can use for any practical use.
If you have the background, I suggest getting familiar with some work by Alexander Koller from Saarland University. He describes how to code NLG to PDDL. The main article you'll want to read is "Sentence generating as a planning problem".
If you do not have any background in NLP, just search for the online courses or course materials by Michael Collings or Dan Jurafsky.
Writing random sentences is not that hard. Any parser textbook's simple-english-grammar example can be run in reverse to generate grammatically correct nonsense sentences.
Another way is the word-tuple-random-walk, made popular by the old BYTE magazine TRAVESTY, or stuff like
http://www.perlmonks.org/index.pl?node_id=94856
I've studied some simple semantic network implementations and basic techniques for parsing natural language. However, I haven't seen many projects that try and bridge the gap between the two.
For example, consider the dialog:
"the man has a hat"
"he has a coat"
"what does he have?" => "a hat and coat"
A simple semantic network, based on the grammar tree parsing of the above sentences, might look like:
the_man = Entity('the man')
has = Entity('has')
a_hat = Entity('a hat')
a_coat = Entity('a coat')
Relation(the_man, has, a_hat)
Relation(the_man, has, a_coat)
print the_man.relations(has) => ['a hat', 'a coat']
However, this implementation assumes the prior knowledge that the text segments "the man" and "he" refer to the same network entity.
How would you design a system that "learns" these relationships between segments of a semantic network? I'm used to thinking about ML/NL problems based on creating a simple training set of attribute/value pairs, and feeding it to a classification or regression algorithm, but I'm having trouble formulating this problem that way.
Ultimately, it seems I would need to overlay probabilities on top of the semantic network, but that would drastically complicate an implementation. Is there any prior art along these lines? I've looked at a few libaries, like NLTK and OpenNLP, and while they have decent tools to handle symbolic logic and parse natural language, neither seems to have any kind of proabablilstic framework for converting one to the other.
There is quite a lot of history behind this kind of task. Your best start is probably by looking at Question Answering.
The general advice I always give is that if you have some highly restricted domain where you know about all the things that might be mentioned and all the ways they interact then you can probably be quite successful. If this is more of an 'open-world' problem then it will be extremely difficult to come up with something that works acceptably.
The task of extracting relationship from natural language is called 'relationship extraction' (funnily enough) and sometimes fact extraction. This is a pretty large field of research, this guy did a PhD thesis on it, as have many others. There are a large number of challenges here, as you've noticed, like entity detection, anaphora resolution, etc. This means that there will probably be a lot of 'noise' in the entities and relationships you extract.
As for representing facts that have been extracted in a knowledge base, most people tend not to use a probabilistic framework. At the simplest level, entities and relationships are stored as triples in a flat table. Another approach is to use an ontology to add structure and allow reasoning over the facts. This makes the knowledge base vastly more useful, but adds a lot of scalability issues. As for adding probabilities, I know of the Prowl project that is aimed at creating a probabilistic ontology, but it doesn't look very mature to me.
There is some research into probabilistic relational modelling, mostly into Markov Logic Networks at the University of Washington and Probabilstic Relational Models at Stanford and other places. I'm a little out of touch with the field, but this is is a difficult problem and it's all early-stage research as far as I know. There are a lot of issues, mostly around efficient and scalable inference.
All in all, it's a good idea and a very sensible thing to want to do. However, it's also very difficult to achieve. If you want to look at a slick example of the state of the art, (i.e. what is possible with a bunch of people and money) maybe check out PowerSet.
Interesting question, I've been doing some work on a strongly-typed NLP engine in C#: http://blog.abodit.com/2010/02/a-strongly-typed-natural-language-engine-c-nlp/ and have recently begun to connect it to an ontology store.
To me it looks like the issue here is really: How do you parse the natural language input to figure out that 'He' is the same thing as "the man"? By the time it's in the Semantic Network it's too late: you've lost the fact that statement 2 followed statement 1 and the ambiguity in statement 2 can be resolved using statement 1. Adding a third relation after the fact to say that "He" and "the man" are the same is another option but you still need to understand the sequence of those assertions.
Most NLP parsers seem to focus on parsing single sentences or large blocks of text but less frequently on handling conversations. In my own NLP engine there's a conversation history which allows one sentence to be understood in the context of all the sentences that came before it (and also the parsed, strongly-typed objects that they referred to). So the way I would handle this is to realize that "He" is ambiguous in the current sentence and then look back to try to figure out who the last male person was that was mentioned.
In the case of my home for example, it might tell you that you missed a call from a number that's not in its database. You can type "It was John Smith" and it can figure out that "It" means the call that was just mentioned to you. But if you typed "Tag it as Party Music" right after the call it would still resolve to the song that's currently playing because the house is looking back for something that is ITaggable.
I'm not exactly sure if this is what you want, but take a look at natural language generation wikipedia, the "reverse" of parsing, constructing derivations that conform to the given semantical constraints.
I need to parse recipe ingredients into amount, measurement, item, and description as applicable to the line, such as 1 cup flour, the peel of 2 lemons and 1 cup packed brown sugar etc. What would be the best way of doing this? I am interested in using python for the project so I am assuming using the nltk is the best bet but I am open to other languages.
I actually do this for my website, which is now part of an open source project for others to use.
I wrote a blog post on my techniques, enjoy!
http://blog.kitchenpc.com/2011/07/06/chef-watson/
The New York Times faced this problem when they were parsing their recipe archive. They used an NLP technique called linear-chain condition random field (CRF). This blog post provides a good overview:
"Extracting Structured Data From Recipes Using Conditional Random Fields"
They open-sourced their code, but quickly abandoned it. I maintain the most up-to-date version of it and I wrote a bit about how I modernized it.
If you're looking for a ready-made solution, several companies offer ingredient parsing as a service:
Zestful (full disclosure: I'm the author)
Spoonacular
Edamam
I guess this is a few years out, but I was thinking of doing something similar myself and came across this, so thought I might have a stab at it in case it is useful to anyone else in f
Even though you say you want to parse free test, most recipes have a pretty standard format for their recipe lists: each ingredient is on a separate line, exact sentence structure is rarely all that important. The range of vocab is relatively small as well.
One way might be to check each line for words which might be nouns and words/symbols which express quantities. I think WordNet may help with seeing if a word is likely to be a noun or not, but I've not used it before myself. Alternatively, you could use http://en.wikibooks.org/wiki/Cookbook:Ingredients as a word list, though again, I wouldn't know exactly how comprehensive it is.
The other part is to recognise quantities. These come in a few different forms, but few enough that you could probably create a list of keywords. In particular, make sure you have good error reporting. If the program can't fully parse a line, get it to report back to you what that line is, along with what it has/hasn't recognised so you can adjust your keyword lists accordingly.
Aaanyway, I'm not guaranteeing any of this will work (and it's almost certain not to be 100% reliable) but that's how I'd start to approach the problem
This is an incomplete answer, but you're looking at writing up a free-text parser, which as you know, is non-trivial :)
Some ways to cheat, using knowledge specific to cooking:
Construct lists of words for the "adjectives" and "verbs", and filter against them
measurement units form a closed set, using words and abbreviations like {L., c, cup, t, dash}
instructions -- cut, dice, cook, peel. Things that come after this are almost certain to be ingredients
Remember that you're mostly looking for nouns, and you can take a labeled list of non-nouns (from WordNet, for example) and filter against them.
If you're more ambitious, you can look in the NLTK Book at the chapter on parsers.
Good luck! This sounds like a mostly doable project!
Can you be more specific what your input is? If you just have input like this:
1 cup flour
2 lemon peels
1 cup packed brown sugar
It won't be too hard to parse it without using any NLP at all.