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The article was a disappointment: only one sentence was dedicated to motivating the business problem, and there was no description of the application. People a

by dlkf 7y ago

The article was a disappointment: only one sentence was dedicated to motivating the business problem, and there was no description of the application. People act like the utility of this stuff is apriori obvious, but to me it's really not. Unless you are trying to build a knowledge graph, what do you do with these triples? (And even if you are, you need humans to independently vet them for noise induced by the source material, as well as your algo. My understanding is that information architects and annotators are essential to building and maintaining Google's KG.) Article summarization is often cited and I find this unconvincing. Most articles can't be reduced to a trite collection of triples. They have a higher level thesis that would be AI complete to parse. What are some other examples?

6gvONxR4sf7o 7y ago
You might not be the intended audience.
parksy 7y ago
The main problem these days is information is generated much more quickly than it can be classified. Short of forcing everyone in the world to adopt a standard for semantic markup for everything they publish, which will almost certainly never happen, the alternative is to use algorithms to extract and classify entities and relationships in unstructured data. Logistically it would be near impossible to manually vet every single automatically classified piece of data, but it would be possible to assume a % accuracy based on known training sets representing a realistic cross section of the type of data being classified. Once the data is categorised and related across numerous taxonomy, within a certain degree of error, a great deal of useful actions are possible. Say you want to fetch all opinion pieces where the subject is a certain political party, then run a sentiment analysis to see if they are largely favourable or unfavourable, then drill down to see what aspects cause opinion to sway (could it be the gender of the author, the age, the country) etc. Even with a certain degree of error, this kind of analysis would be very valuable for strategists, reporters, researchers, etc. The same could be done for stock markets with companies, shareholders and traders all being able to delve into the masses of data being poured daily into the web. Through network analysis (traversing the knowledge graph) you'd be able to identify clusters of similar information, to spot a smear campaign or a sudden surge of bot-generated "fake news" in the making or otherwise identify risks and opportunities in near real-time. To be able to do this, you need some way of extracting meaning from random blobs of text. There's already a raft of services out there that offer some form of knowledge extraction and sentiment analysis - improving the accuracy of the underlying algorithms gives a competitive edge and makes the services more valuable over time. This includes developing AI that can interpret the higher level thesis with reasonable accuracy (keep in mind many humans fail miserably at this too) and how to avoid purposeful attempts to fool or spam the system. These are all problems that can be tackled as data research continues. Even for a researcher in any field, being able to upload your thesis, have it analysed and classified, then using the metadata to explore similar research would be advantageous. You may find connections between unlikely fields of research that might have been overlooked with standard keyword relevancy searches, which could go on to spawn new areas to study or new considerations for existing research. Quite often advancements come when disparate fields are bridged, and having tools to help automate the process (even if they aren't 100% accurate) would be very useful. I doubt we're close to replacing the human element just yet, but having at our disposal better tools to sift through the petabytes of crap on the internet is a highly sought after and valuable outcome, and much more than an intellectual exercise.
GistNoesis 7y ago
Extracting triplets is useful for explain-ability. You know where you took the fact from, so it help build a consistent view of the world. In fact this consistency can be used as a signal for training your algorithms in an unsupervised way. It is useful for building a search engine because it helps create relevant index automatically. Once you have a way to construct the knowledge graph automatically, you can made better recommendations. One usually good recommendation is inferring the graph query which gave some results the user was interested in. Then you can run the query and propose to the user an exhaustive set of similar results. Basically it's a great way to intuit the intent of the user : It helps you get the "Why". An other application, which is a little extension is to use quad-stores instead of triplets, so you can have facts about the facts. It is very useful to highlight the fake news/fake users. Or to identify the point of view of various individuals for further targeting. The more promising ventures of this type of work is that Graph Query Language are kind of an easy programming language and are a good stepping stone toward more useful programming languages. This is probably one step on the path toward program induction.
calhoun137 7y ago
> People act like the utility of this stuff is apriori obvious, but to me it's really not. Unless you are trying to build a knowledge graph, what do you do with these triples? I have been very interested in this subject for years, the motivation to me seems clear, so perhaps I can shed some light on this for you. Being able to extract information from raw text would have a huge number of business applications, and could completely change the way human beings interact with machines. Besides that, there are a number of questions which are unanswered which bear on fundamental research topics in deep learning, artificial intelligence, and natural language processing. The idea behind the triples is they provide additional data points which can be used to improve the efficiency & accuracy of information extraction. This is a super hard subject at the forefront of modern research and its just a hobby of mine to keep up to date on the state of affairs at this point, having long ago given up the side project I had been working on. I must not have been keeping up very well because I had not seen the link before despite being from 2016.
polm23 7y ago
I think that in many cases this is genuinely useless, but there are some good and even important applications of the process. An example is health records. Doctors enter short notes in a text editor for a lot of these, and being able to extract information discover symptom associations using information extraction is much faster than manually going over data. Here's a good presentation that covers some of this (PDF): https://people.csail.mit.edu/regina/talks/CNLP.pdf https://people.csail.mit.edu/regina/talks/CNLP.pdf
- reubens 7y ago
  Thank you for the link. That was enlightening.

by dlkf 7y ago

6gvONxR4sf7o 7y ago

You might not be the intended audience.

parksy 7y ago

The main problem these days is information is generated much more quickly than it can be classified. Short of forcing everyone in the world to adopt a standard for semantic markup for everything they publish, which will almost certainly never happen, the alternative is to use algorithms to extract and classify entities and relationships in unstructured data. Logistically it would be near impossible to manually vet every single automatically classified piece of data, but it would be possible to assume a % accuracy based on known training sets representing a realistic cross section of the type of data being classified. Once the data is categorised and related across numerous taxonomy, within a certain degree of error, a great deal of useful actions are possible. Say you want to fetch all opinion pieces where the subject is a certain political party, then run a sentiment analysis to see if they are largely favourable or unfavourable, then drill down to see what aspects cause opinion to sway (could it be the gender of the author, the age, the country) etc. Even with a certain degree of error, this kind of analysis would be very valuable for strategists, reporters, researchers, etc. The same could be done for stock markets with companies, shareholders and traders all being able to delve into the masses of data being poured daily into the web. Through network analysis (traversing the knowledge graph) you'd be able to identify clusters of similar information, to spot a smear campaign or a sudden surge of bot-generated "fake news" in the making or otherwise identify risks and opportunities in near real-time. To be able to do this, you need some way of extracting meaning from random blobs of text. There's already a raft of services out there that offer some form of knowledge extraction and sentiment analysis - improving the accuracy of the underlying algorithms gives a competitive edge and makes the services more valuable over time. This includes developing AI that can interpret the higher level thesis with reasonable accuracy (keep in mind many humans fail miserably at this too) and how to avoid purposeful attempts to fool or spam the system. These are all problems that can be tackled as data research continues. Even for a researcher in any field, being able to upload your thesis, have it analysed and classified, then using the metadata to explore similar research would be advantageous. You may find connections between unlikely fields of research that might have been overlooked with standard keyword relevancy searches, which could go on to spawn new areas to study or new considerations for existing research. Quite often advancements come when disparate fields are bridged, and having tools to help automate the process (even if they aren't 100% accurate) would be very useful. I doubt we're close to replacing the human element just yet, but having at our disposal better tools to sift through the petabytes of crap on the internet is a highly sought after and valuable outcome, and much more than an intellectual exercise.

GistNoesis 7y ago

Extracting triplets is useful for explain-ability. You know where you took the fact from, so it help build a consistent view of the world. In fact this consistency can be used as a signal for training your algorithms in an unsupervised way. It is useful for building a search engine because it helps create relevant index automatically. Once you have a way to construct the knowledge graph automatically, you can made better recommendations. One usually good recommendation is inferring the graph query which gave some results the user was interested in. Then you can run the query and propose to the user an exhaustive set of similar results. Basically it's a great way to intuit the intent of the user : It helps you get the "Why". An other application, which is a little extension is to use quad-stores instead of triplets, so you can have facts about the facts. It is very useful to highlight the fake news/fake users. Or to identify the point of view of various individuals for further targeting. The more promising ventures of this type of work is that Graph Query Language are kind of an easy programming language and are a good stepping stone toward more useful programming languages. This is probably one step on the path toward program induction.

calhoun137 7y ago

> People act like the utility of this stuff is apriori obvious, but to me it's really not. Unless you are trying to build a knowledge graph, what do you do with these triples? I have been very interested in this subject for years, the motivation to me seems clear, so perhaps I can shed some light on this for you. Being able to extract information from raw text would have a huge number of business applications, and could completely change the way human beings interact with machines. Besides that, there are a number of questions which are unanswered which bear on fundamental research topics in deep learning, artificial intelligence, and natural language processing. The idea behind the triples is they provide additional data points which can be used to improve the efficiency & accuracy of information extraction. This is a super hard subject at the forefront of modern research and its just a hobby of mine to keep up to date on the state of affairs at this point, having long ago given up the side project I had been working on. I must not have been keeping up very well because I had not seen the link before despite being from 2016.

polm23 7y ago

I think that in many cases this is genuinely useless, but there are some good and even important applications of the process. An example is health records. Doctors enter short notes in a text editor for a lot of these, and being able to extract information discover symptom associations using information extraction is much faster than manually going over data. Here's a good presentation that covers some of this (PDF): https://people.csail.mit.edu/regina/talks/CNLP.pdf https://people.csail.mit.edu/regina/talks/CNLP.pdf

reubens 7y ago
Thank you for the link. That was enlightening.