What is the difference between named entity recognition and named entity resolution? Would appreciate a practical example.
Named entity recognition is picking up the names and classifying them in running text. E.g., given (1)
John Terry to face criminal charges over alleged racist abuse
an NE recognizer will output
[PER John Terry] to face criminal charges over alleged racist abuse
NE resolution or normalization means finding out which entity in the outside world a name refers to. E.g., in the above example, the output would be annotated with a unique identifier for the footballer John Terry, like his Wikipedia URL:
[https://en.wikipedia.org/wiki/John_Terry John Terry] to face criminal charges
over alleged racist abuse
as opposed to, e.g.
https://en.wikipedia.org/wiki/John_Terry_%28actor%29
https://en.wikipedia.org/wiki/John_Terry_%28baseball%29
or any of the other John Terry's the Wikipedia knows.
Related
I'm using Dialogflow as the NLP engine behind a chatbot, and am trying to get it to recognize company names. In the following examples, it understands the intent well, but doesn't pick up the company name.
Create a company called Google
Make a new account called Johnson & Johnson
New company Nike
Does anyone have any advice on how I can get Dialogflow to start to recognize these entities? I'm wondering if there are features I don't know about, or maybe some sort of plugin/library I can utilize for this?
I'm afraid there's no Dialogflow system entity that can do this for you as of Oct, 2020. Your best bet is to add as many training phrases as possible and create a custom entity with #sys.any as the entity type. Annotate as many training phrases as possible and let Dialogflow do the rest. When it comes to identifying company names specifically, there are two types of company names:-
Common company names like "Google", "Facebook" which Dialogflow can recognize without much assistance, especially if your entity type is #sys.any.
Domain-specific company names like Overflow LLC or Stack and Overflow Associates. Here, the annotated training phrases play an important role and if you have an idea of the types of companies that would need to be understood, it would help annotating those phrases (Eg: LLC, Associates, Firm etc).
Also think about how you structure your question to ensure the user enters values as per your needs. Eg: Please type in/spell out the name of your company increases the chances that anything your user enters would just be the company name.
How do I train a Watson Knowledge Studio machine learning annotator to identify education info that is not a part of a proper sentence. For example, two bullet points. How do I form a type system that will identify entities without breaking them all apart? I've considered using relation annotations, but according to the official documentation relation types should only be annotated if the sentence specifically mentions the relation. Such as "Mary works for IBM" is an example of the employedBy relation type. (Mary employedBy IBM) However, their own videos show them annotating "Ford F-150" with a manufacturedBy relation even though the sentence doesn't specifically state the relation. For example, "The Ford F-150 struck a light pole." (F-150 manufacturedBy Ford)
This is the kind of text I'm working with:
B.A., City University of New York, 1995
M.A., New York University, 1997
Ph.D, Columbia University, 1999
I could annotate these with degree, school, and graduationYear entities, but I'll end up getting back "1995", "1997", "1999" "B.A.", "City University of New York", "Columbia University", "M.A.", "New York University", "Ph.D"; a jumble that I can't work with because I can't tell anymore what degree belongs with what school belongs with what graduation year.
As for the expressions which include two bullet points, there is a possibility to improve accuracy to detect sentences as they can work with WKS, using Dictionary-based Tokenizer.
https://console.bluemix.net/docs/services/knowledge-studio/create-project.html#wks_tokenizer
I imported your example text to WKS and checked the result of tokenization, and then the expression was separated into 3 sentences.
In this case you can annotate relations among degree, school and graduation year.
I am trying to develop software to get suitable attributes for entities names depending on entity type.
For example if I have entities such doctor, nurse, employee , customer, patient , lecturer , donor, user, developer, designer, driver, passenger and technician, they all will have attributes such as name, sex, date of birth , email address, home address and telephone number because all of them are people.
Second example word such as university, college, hospital, hotel and supermarket can share attributes such as name, address and telephone number because all of them could be organization.
Are there any Natural Language Processing tools and software could help me to achieve my goal.
I need to identify entity type as person or origination then I attached suitable attributes according to the entity type?
I have looked at Name Entity Recognition (NER) tool such as Stanford Name Entity recognizer which can extract Entity such as Person, Location, Organization, Money, time, Date and Percent But it was not really useful.
I can do it by building my own gazetteer however I do not prefer to go to this option unless I failed to do it automatically.
Any helps, suggestions and ideas will be appreciated.
If I understand correctly, you are mainly interested in knowing if a given word can be mapped to a general category of Human, Organization, etc.
You should use WordNet, which provides a complete hierarchy of the general English lexicon. Try it a bit in the user interface to get of feel of how it works.
WordNet encodes relations between words. One of these relation is hypernymy, a fancy word that means a relation of general-to-particular.
Some examples:
Vehicle is a hypernym of boat.
Vehicle is a hypernem of car.
Human is a hypernym of worker which is a hypernym of plumber.
Hyponymy is the inverse relation of hypernymy:
Boat is a hyponym of vehicle.
Car is a hyponym of vehicle.
Plumber is a hyponym of worker, itself a hyponym of human.
These relations are transitive, so in my last example plumber is also a hyponym of human. This gives you the solution to your problem: any word that has human as hypernym should be mapped to Human and have people attributes.
There are libraries to access WordNet from Java and Python, as well as from many other languages. Here is the documentation for using WordNet with the NLTK Python module.
A short example to determine if a word is hyponym of "human"
from nltk.corpus import wordnet as wn
human = wn.synset('person.n.01')
hyponyms_of_human = set(x for x in human.closure(lambda s:s.hyponyms())
fireman = wn.synsets('fireman')
salad = wn.synsets('salad')
print(any(x in hyponyms_of_human for x in fireman)) # outputs True
print(any(x in hyponyms_of_human for x in salad)) # outputs False
In the modeler, relationship is represented by a line between two entities. It would be no problem if the relationship has no descriptive attributes. But if it has, how can I represent the descriptive attributes? For example, the relationship set advisor, between entity set student and entity set instructor, has a descriptive attribute date to record the data an instructor become the advisor of a student. How I can represent the attribute?
A relationship can be viewed as an assertion. I believe the assertion that represents the relationship here is: An instructor acts as an adviser to a student.
There are 3 nouns in the assertion which implies that there are 3 entities involved in the relation:
Instructor
Student
Adviser
There are 2 fundamental entities (Student and Instructor) upon which the associative entity (Adviser) depends. In other words, an instance of Adviser needs an instance of Instructor and Student to make sense.
The simple answer is to simply make the date an attribute of Adviser. Unfortunately, life is often not that simple.
Are are the following two assertions valid?:
Jim acts as an adviser to Jane from 01/01/2009 to 06/30/2009.
Jim acts as an adviser to Jane from 01/01/2011 to 06/30/2011.
If so, then a new entity (Advisory Period) is required. An Advisory Period the amount of time during which an instructor acts as an adviser to a student.
The Advisory Period entity would be dependent on Adviser (necessitating a dependent 1:m relation between Adviser and Advisory Period) and the start and end dates of the period would be recorded as non-key attributes of the Advisory Period.
Hope this helps
Sorry, I know this is a very lame question to ask and not of any use to anyone else. I have an assignment in UML due tomorrow and I don't even know the basics (all-nighter ahead!). I'm not looking for a walkthrough, I simply want your opinion on something. The assignment is as follows (you only need to skim over it!):
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Gourmet Surprise (GS) is a small catering firm with five employees. During a typical weekend, GS caters fifteen events with twenty to fifty people each. The business has grown rapidly over the past year and the owner wants to install a new computer system for managing the ordering and buying process. GS has a set of ten standard menus. When potential customers call, the receptionist describes the menus to them. If the customer decides to book an event (dinner, lunch, picnic, finger food etc.), the receptionist records the customer information (e.g., name, address, phone number, etc.) and the information about the event (e.g., place, date, time, which one of the standard menus, total price) on a contract. The customer is then faxed a copy of the contract and must sign and return it along with a deposit (often a credit card or by check) before the event is officially booked. The remaining money is collected when the catering is delivered. Sometimes, the customer wants something special (e.g., birthday cake). In this case, the receptionist takes the information and gives it to the owner who determines the cost; the receptionist then calls the customer back with the price information. Sometimes the customer accepts the price, other times, the customer requests some changes that have to go back to the owner for a new cost estimate. Each week, the owner looks through the events scheduled for that weekend and orders the supplies (e.g., plates) and food (e.g., bread, chicken) needed to make them. The owner would like to use the system for marketing as well. It should be able to track how customers learned about GS, and identify repeat customers, so that GS can mail special offers to them. The owner also wants to track the events on which GS sent a contract, but the customer never signed the contract and actually booked a GS.
Exercise:
Create an activity diagram and a use case model (complete with a set of detail use case descriptions) for the above system. Produce an initial domain model (class diagram) based on these descriptions.
Elaborate the use cases into sequence diagrams, and include any state diagrams necessary. Finally use the information from these dynamic models to expand the domain model into a full application model.
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In your opinion, do you think this question is asking me to come up with a package for an online ordering system to replace the system described above, or to create UML diagrams that facilitate the existing telephone-based system?
Create an activity diagram and a use case model (complete with a set of detail use case descriptions) for the above system.
I think it's right there in the text: they want you to document the system described.
Best of luck!