NLP general English to action
Question:
I am working on automating task flow of application using text based Natural Language Processing.
It is something like chatting application where the user can type in the text area. At same time python code interprets what user wants and it performs the corresponding action.
Application has commands/actions like:
- Create Task
- Give Name to as t1
- Add time to task
- Connect t1 to t2
The users can type in chat (natural language). It will be like a general English conversation, for example:
- Can you create a task with name t1 and assign time to it. Also, connect t1 to t2
I could write a rule drive parser, but it would be limited to few rules only.
Which approach or algorithm can I use to solve this task?
How can I map general English to command or action?
Answers:
I think your issue is related to Rule-based system (Wiki).
You need to two basic components in core of project like this:
1- Role base:
list of your roles.
2- Inference engine:
infers information or takes action based on the interaction of input and the rule base.
spacy is python approach that I think it will help you. (More information).
I think the best solution would be to use an external service like API.ai or wit.ai. You can create a free account and then you can map certain texts to so-called ‘intents’.
These intents define the main actions of your system. You can also define ‘entities’ that would capture, for instance, the name of the task. Please have a look at these tools. I’m sure they can handle your use case.
You may want to try nltk. This is an excellent library for NLP and comes with a handy book to get you started. I think you may find chapter 8 helpful for finding sentence structure, and chapter 7 useful for figuring out what your user is requesting the bot to do. I would recommend you read the entire thing if you have more than a passing interest in NLP, as most of it is quite general and can be applied outside of NLTK.
What you are describing is a general problem with quite a few possible solutions. Your business requirements, which we do not know, are going to heavily influence the correct approach.
For example, you will need to tokenize the natural language input. Should you use a rules-based approach, or a machine learning one? Maybe both? Let’s consider your input string:
Can you create a task with name t1 and assign time to it. Also, connect t1 to t2
Our system might tokenize this input in the following manner:
Can you [create a task] with [name] [t1] and [assign] [time] to it. Also, [connect] [t1] to [t2]
The brackets indicate semantic information, entirely without structure. Does the structure matter? Do you need to know that connect t1 is related to t2 in the text itself, or can we assume that it is because all inputs are going to follow this structure?
If the input will always follow this structure, and will always contain these kinds of semantics, you might be able to get away with parsing this using regular expressions and feeding prebuilt methods.
If the input is instead going to be true natural language (ie, you are building a siri or alexa competitor) then this is going to be wildly more complex, and you aren’t going to get a useful answer in a SO post like this. You would instead have a few thousand SO posts ahead of you, assuming you have sufficient familiarity with both linguistics and computer science to allow you to approach the problem systematically.
Lets say text is “Please order a pizza for me” or “May I have a cab booking from uber”
Use a good library like nltk and parse these sentences. As social English is generally grammatically incorrect, you might have to train your parser with your custom broken English corpora. Next, These are the steps you have to follow to get an idea about what a user wants.
- Find out the full stop’s in a paragraph, keeping in mind the abbreviations, lingos like …., ??? etc.
- Next find all the verbs and noun phrases in individual sentences can be done through POS(part of speech tagging) by different libraries.
After that the real work starts, My approach would be to create a graph of verbs where similar verbs are close to each other and dissimilar verbs are very far off.
Lets say you have words like arrange, instruction , command, directive, dictate which are closer to order. So if your user writes any one of the above verbs in their text , your algorithm will identify that user really means to imply order. you can also use edges of that graph to specify the context in which the verb was used.
Now, you have to assign action to this verb “order” based on the noun phrase which were parsed in the original sentence.
This is just a high level explanation of this algorithm, it has many problems which needs serious considerations, some of them are listed below.
- Finding similarity index between root_verb and the given verb in very short time.
- New words who doesn’t have an entry in the graph. A possible approach is to update your graph by searching google for this word, find a context from the pages on which it was mentioned and find an appropriate place for this new word in the graph.
- Similarity indexes of misspelled words with proper verbs or nouns.
If you want to build a more sophisticated model, you can construct graph for every part of speech and can select appropriate words from each graph to form sentences in response to the queries. Above mentioned graph is meant for Verb Part of speech.
Although, @whrrgarbl is right. It seems like you do not want to train a bot.
So, then to handle language input variations(lexical, semantic..) you would need a pre-trained bot which you can customize(or may be just add rules according to your need).
The easiest business oriented solution is Amazon Lex. There is a free preview program too.
Another option would be to use Google’s Parsey McParseface(a pre-trained English parser, there is support for 40 languages) and integrate it with a chat-framework. Here is a link to a python repo, where the author claims to have made the installation and training process convenient.
Lastly, this provides a comparison of various chatbot platforms.
I am working on automating task flow of application using text based Natural Language Processing.
It is something like chatting application where the user can type in the text area. At same time python code interprets what user wants and it performs the corresponding action.
Application has commands/actions like:
- Create Task
- Give Name to as t1
- Add time to task
- Connect t1 to t2
The users can type in chat (natural language). It will be like a general English conversation, for example:
- Can you create a task with name t1 and assign time to it. Also, connect t1 to t2
I could write a rule drive parser, but it would be limited to few rules only.
Which approach or algorithm can I use to solve this task?
How can I map general English to command or action?
I think your issue is related to Rule-based system (Wiki).
You need to two basic components in core of project like this:
1- Role base:
list of your roles.
2- Inference engine:
infers information or takes action based on the interaction of input and the rule base.
spacy is python approach that I think it will help you. (More information).
I think the best solution would be to use an external service like API.ai or wit.ai. You can create a free account and then you can map certain texts to so-called ‘intents’.
These intents define the main actions of your system. You can also define ‘entities’ that would capture, for instance, the name of the task. Please have a look at these tools. I’m sure they can handle your use case.
You may want to try nltk. This is an excellent library for NLP and comes with a handy book to get you started. I think you may find chapter 8 helpful for finding sentence structure, and chapter 7 useful for figuring out what your user is requesting the bot to do. I would recommend you read the entire thing if you have more than a passing interest in NLP, as most of it is quite general and can be applied outside of NLTK.
What you are describing is a general problem with quite a few possible solutions. Your business requirements, which we do not know, are going to heavily influence the correct approach.
For example, you will need to tokenize the natural language input. Should you use a rules-based approach, or a machine learning one? Maybe both? Let’s consider your input string:
Can you create a task with name t1 and assign time to it. Also, connect t1 to t2
Our system might tokenize this input in the following manner:
Can you [create a task] with [name] [t1] and [assign] [time] to it. Also, [connect] [t1] to [t2]
The brackets indicate semantic information, entirely without structure. Does the structure matter? Do you need to know that connect t1 is related to t2 in the text itself, or can we assume that it is because all inputs are going to follow this structure?
If the input will always follow this structure, and will always contain these kinds of semantics, you might be able to get away with parsing this using regular expressions and feeding prebuilt methods.
If the input is instead going to be true natural language (ie, you are building a siri or alexa competitor) then this is going to be wildly more complex, and you aren’t going to get a useful answer in a SO post like this. You would instead have a few thousand SO posts ahead of you, assuming you have sufficient familiarity with both linguistics and computer science to allow you to approach the problem systematically.
Lets say text is “Please order a pizza for me” or “May I have a cab booking from uber”
Use a good library like nltk and parse these sentences. As social English is generally grammatically incorrect, you might have to train your parser with your custom broken English corpora. Next, These are the steps you have to follow to get an idea about what a user wants.
- Find out the full stop’s in a paragraph, keeping in mind the abbreviations, lingos like …., ??? etc.
- Next find all the verbs and noun phrases in individual sentences can be done through POS(part of speech tagging) by different libraries.
After that the real work starts, My approach would be to create a graph of verbs where similar verbs are close to each other and dissimilar verbs are very far off.
Lets say you have words like arrange, instruction , command, directive, dictate which are closer to order. So if your user writes any one of the above verbs in their text , your algorithm will identify that user really means to imply order. you can also use edges of that graph to specify the context in which the verb was used.
Now, you have to assign action to this verb “order” based on the noun phrase which were parsed in the original sentence.
This is just a high level explanation of this algorithm, it has many problems which needs serious considerations, some of them are listed below.
- Finding similarity index between root_verb and the given verb in very short time.
- New words who doesn’t have an entry in the graph. A possible approach is to update your graph by searching google for this word, find a context from the pages on which it was mentioned and find an appropriate place for this new word in the graph.
- Similarity indexes of misspelled words with proper verbs or nouns.
If you want to build a more sophisticated model, you can construct graph for every part of speech and can select appropriate words from each graph to form sentences in response to the queries. Above mentioned graph is meant for Verb Part of speech.
Although, @whrrgarbl is right. It seems like you do not want to train a bot.
So, then to handle language input variations(lexical, semantic..) you would need a pre-trained bot which you can customize(or may be just add rules according to your need).
The easiest business oriented solution is Amazon Lex. There is a free preview program too.
Another option would be to use Google’s Parsey McParseface(a pre-trained English parser, there is support for 40 languages) and integrate it with a chat-framework. Here is a link to a python repo, where the author claims to have made the installation and training process convenient.
Lastly, this provides a comparison of various chatbot platforms.