We use the Azure Batch Transcription Service to get the Transcript of an Audio / Speech.
In here we noticed, that sometimes filler words like "uhm", "hm" or something similar are included, but very rarely - also as we used this service for a few months already and we have the feeling as if it "got less" (so less "uhm"s in the transcript)
Q1: Is there a way to get the fill words? We want to recieve them within the transcript.
Also, as we sometimes record conversations it can happen that someone says a name or is talking about other personal information.
Q2: Is there a way to "filter" those personal information / words within the transcript?
Sorry, I don't think there is a way to filter personal data/ word when translate. We only can do profanity Filter for batch transcription.
But I agree this feature will be very helpful. I will forward this feature request to product group to see if we can have this in the feature.
Thing I will suggest is to optimize the transcription as last to filter the sensitive information.
Regards,
Yutong
Related
I'm trying to ascertain the "right tool for the job" here, and I believe Cognitive Services can do this but without disappearing down an R&D rabbit-hole I thought I'd make sure I was tunnelling in the right direction first.
So, here is the brief:
I have a collection of known existing phrases which I want to look for, but these might be written in slightly different ways, be that grammar or language.
I want to be able to parse a (potentially large) volume of text to scan and look for those phrases so that I can identify them.
For example, my phrase could be "the event will be in person" but that also needs to identify different uses of language; for example "in-person event", "face to face event", or "on-site event" - as well as the various synonyms and variations you can get with such things.
LUIS initially appeared to be the go-to tool for this kind of thing, and includes the ability to write your own Features (aka Phrase Lists) to augment the model, but it isn't clear whether that would hit the brief - LUIS appears to be much more about "intent" and user interaction (for example building a chat Bot, or understanding intent from emails).
Text Analytics also seems a likely candidate, but again seems more focused about identifying "entities" (such as people / places / organisations) rather than a natural language "phrase" - would this tool work if I was defining my own "Topics" or is that really just barking up the wrong tree?
.. or ... is there actually something else I should be looking at completely different?
At this point - I'm really looking for a "which tool should I spend lots of time learning about".
Thanks all in advance - I appreciate this is a fairly open-ended requirement.
It seems your scenario aligns more with our text analytics service. I was going to recommend Key Phrase Extraction API which evaluates unstructured text and returns a list of key phrases. However, since you require to use known (custom) phrase list, it may not be the solution you're looking for. We currently don't support custom key phrase extraction today, however it's on our roadmap. If interested, we can connect you with the product team to learn more about your scenario.
Updated:
Please try custom NER capability.
In Salesforce's Service Cloud one can enable the out of the box search function where the user enters a term and the system searches all parts of the database for a match. I would like to enable smart searching of acronyms so that if I spell an organizations name the search functionality will also search for associated acronyms in the database. For example, if I search type in American Automobile Association, I would also get results that contain both "American Automobile Association" and "AAA".
I imagine such a script would involve declaring that if the term being searched contains one or more spaces or periods, take the first letter of the first word and concatenate it with the letters that follow subsequent spaces or periods.
I have unsuccessfully tried to find scripts for this or articles on enabling this functionality in Salesforce. Any guidance would be appreciated.
Interesting question! I don't think there's a straightforward answer but as it's standard search functionality, not 100% programming related - you might want to cross-post it to salesforce.stackexchange.com
Let's start with searchable fields list: https://help.salesforce.com/articleView?id=search_fields_business_accounts.htm&type=0
In Setup there's standard functionality for Synonyms, quite easy to use. It's not a silver bullet though, applies only to certain objects like Knowledge Base (if you use it). Still - it claims to work on Cases too so if there's "AAA" in Case description it should still be good enough?
You could also check out the trick with marking a text field as indexed and/or external ID and adding there all your variations / acronyms: https://success.salesforce.com/ideaView?id=08730000000H6m2 This is more work, to prepare / sanitize your data upfront but it's not a bad idea.
Similar idea would be to use Tags although that could explode in size very quickly. It's ridiculous to create a tag for every single company.
You can do some really smart things in data deduplication rules. Too much to write it all here, check out the trailhead: https://trailhead.salesforce.com/en/modules/sales_admin_duplicate_management/units/sales_admin_duplicate_management_unit_2 No idea if it impacts search though.
If you suffer from bad address data there are State & Country picklists, no more mess with CA / California / SoCal... https://resources.docs.salesforce.com/204/latest/en-us/sfdc/pdf/state_country_picklists_impl_guide.pdf Might not help with Name problem...
Data.com cleanup might help. Paid service I think, no idea if it affects search too. But if enabling it can bring these common abbreviations into your org - might be better than reinventing the wheel.
I have 20,000 messages (combination of email and live chat) between my customer and my support staff. I also have a knowledge base for my product.
Often times, the questions customers ask are quite simple and my support staff simply point them to the right knowledge base article.
What I would like to do, in order to save my support staff time, is to show my staff a list of articles that may likely be relevant based on the initial user's support request. This way they can just copy and paste the link to the help article instead of loading up the knowledge base and searching for the article manually.
I'm wondering what solutions I should investigate.
My current line of thinking is to run analysis on existing data and use a text classification approach:
For each message, see if there is a response with a link to a how-to article
If Yes, extract key phrases (microsoft cognitive services)
TF-IDF?
Treat each how-to as a 'classification' that belongs to sets of key phrases
Use some supervised machine learning, support vector machines maybe to predict which 'classification, aka how-to article' belongs to key phrase determined from a new support ticket.
Feed new responses back into the set to make the system smarter.
Not sure if I'm over complicating things. Any advice on how this is done would be appreciated.
PS: naive approach of just dumping 'key phrases' into search query of our knowledge base yielded poor results since the content of the help article is often different than how a person phrases their question in an email or live chat.
A simple classifier along the lines of a "spam" classifier might work, except that each FAQ would be a feature as opposed to a single feature classifier of spam, not-spam.
Most spam-classifiers start-off with a dictionary of words/phrases. You already have a start on this with your naive approach. However, unlike your approach a spam classifier does much more than a text search. Essentially, in a spam classifier, each word in the customer's email is given a weight and the sum of weights indicates if the message is spam or not-spam. Now, extend this to as many features as FAQs. That is, features like: FAQ1 or not-FAQ1, FAQ2 or not-FAQ2, etc.
Since your support people can easily identify which of the FAQs an e-mail requires then using a supervised learning algorithm would be appropriate. To reduce the impact of any miss-classification errors, then consider the application presenting a support person with the customer's email followed by the computer generated response and all the support person would have to-do is approve the response or modify it. Modifying a response should result in a new entry in the training set.
Support Vector Machines are one method to implement machine learning. However, you are probably suggesting this solution way too early in the process of first identifying the problem and then getting a simple method to work, as well as possible, before using more sophisticated methods. After all, if a multi-feature spam classifier works why invest more time and money in something else that also works?
Finally, depending on your system this is something I would like to work-on.
I am trying to build a site that searches a database of user comments for the most often mentioned names of movies. However, with certain movie titles like Up and Warrior(2011), there are far too many irrelevant results and I want to only search for the title in threads about movies or else make sure it's mentioned in the right context. Is there a more generalized question that this problem is a subset of (I'm sure there is but google yielded nothing so far).
working out the context of a chunk of text to determin whether the word "up" is refering to a film or not is, unfortunately, something only a human can do at the moment.
have a look at amazon's mechanical turk service, you can pay people to search thru the text for you. this might not be great if you are trying to offer a free service however.
What if I choose to use GMail's awesome mail archive search capabilities on my database? What if, for every transaction that my database is responsible, I emailed details of that transaction to a GMail address that exists for the sole purpose of searching and retrieving transactions.
Anyone logged into that account could search according to labels, invoice numbers, customer names - whatever using Google's search engine. The results are presented as 'email messages'.
Imagine a user working from the standard (web-based) GMail account searches for an invoice number via GMail's search box - he's returned all instances where the db did anything that included that unique number. Opening any of these 'email messages' would have the static text text included at the time of the transactions (historical and tracking gold) but could also carry a Gadget that could transform the 'message' into an editor so as to execute a new transaction on that invoice.
Imagine further that I wasn't the first one to think of this - cuz surely i'm not - and even if i were, i'm not smart enough to execute the idea alone.
Are you aware of efforts similar to this?
thx
[?belongs on superuser instead?]
An interesting idea, however given your search parameters it might be unreliable. Although gmail's search is great, I have found issues when searching for partial terms. Case in point, I had an email whose subject line was "stuffas". When I searched for "stuffa" I got no results, when I searched for "stuffas" I got the email in the search result. Additionally, I had an email with an 8 digit number inside the body. When I searched for 7 digits out of 8, I got no results, but when I put all 8 digits, the email appeared in the results. So, search in gmail may not be as powerful of a solution as you think. Again this is my experience, I'd love to hear if someone is able to partial search numbers in gmail.
I just had the same idea; 4 years after you. It still doesn't look like this has 'been done before' in any production sense. But now in 2014, I really don't see why not. Python packages for interfacing with gmail are already there and dead-simple to use. It does not take a whole lot of abstraction to turn this into a generalized key-value storage.
Its probably not exactly the fastest database, and not the best solution for everything; but as an easy-to-use, easy to search, trivial to configure, 100% uptime, cloud stored and backed up, free-as-in-beer database, its pretty epic as far as I can see.
Anyone else has seen examples of this having been done before?
Edit: having thought about it some more, there are several answers as to why this is a bad idea:
gmail does not permit random access from different locations; it will block you account. quite a showstopper
amazon simpleDB also gives you a simple key-value store with the same characteristics (plus good python support), and isn't THAT big of a pain to set up if you are willing to spend a day wrapping your head around it. And is also effectively free for the kind of traffic that youd be able to cram into a gmail account.