I am looking for some POS tagging web-service. There are many solutions available (mostly in java) that can be integrated but I couldn't find an online service that could do the job.
My problem statement is really simple, I want to send a single word and get back what part of speech it is e.g. Noun, Verb, Adjective etc.
I want to send a single word and get back what part of speech it is e.g. Noun, Verb, Adjective etc.
This is impossible, in English.
A part of speech method would have to take the whole sentence into account to determine the parts of speech of the words.
Some English words are homonyms. They have to be interpreted in context.
Billy read the book.
read, verb
Billy, please give the book to Read.
Read, noun.
Billy, please give the book to Susie to read.
read, verb.
Related
We have a problem here...
We have a text having different patterns of sentences.
We want to get the sentence having a particular word.
Eg:
One further point, by way of providing another model. The analysis in
the second paragraph could lead in the following direction. 'The
Destructors' deals with, obviously, destruction, whilst the book of
Genesis deals with creation. The vocabulary is similar: Blackie
notices that 'chaos had advanced', an ironic reversal of God's
imposing of form on a void. Furthermore, the phrase 'streaks of light
came in through the closed shutters where they worked with the
seriousness of creators', used in the context of destruction, also
parodies the creation of light and darkness in the early passages of
the Biblical book. Greene's ironic use of the vocabulary of the Bible
might be making the point that, for him, the Second World War
signalled the end of a particular Christian era. Now, it is perfectly
arguable that the rise of fascism is linked to this, or that it is the
cause. The cult of personality and secular leadership has, for Greene,
taken over from the key role of the church in Western societies. In
this way the two main themes identified above - the tension between
individual and community, and religion - are linked. In terms of essay
writing this link could well be made after the discussion of the theme
of the individual and the community, and its links with the theme of
leadership. This might be the general conclusion to the essay. After
thoughtful consideration and interpretation a student may well decide
that this is what the (destructors.)' boils down to: Greene is making a
clear link between the rise of fascism and the decline of the Church's
influence. Despite the fact that fascism has been recently defeated,
Greene sees the lack of any contemporary values which could provide
social cohesion as providing the potential for its reappearance.
In the above text, we have bold words (destructors). We want to get the sentences which are having the word "destructors".
The word "destructors" can be present in different formats. Eg: (destructors), (DesTrucTors), (Des.tructors), DESTRUCTORS, destructors, des-tructors.
When we tried writing a regex to match the sentences, we are failing to get the sentences at some conditions(like we are getting half sentences, etc.,).
Could you please help us with this.
If this information doesn't help you to solve, please let us know. Will update it.
Thank you...
I'm not too sure about Python, but I believe this might work:
for match in re.finditer(r"[^.]*destructors[^.]*\.[^\w\s]*", subject, re.IGNORECASE):
# match start: match.start()
# match end (exclusive): match.end()
# matched text: match.group()
In any case, I think the regex you want is:
[^.]*destructors[^.]*\.[^\w\s]*
with the case insensitive and global flags set.
It will be helpful if you could provide the regex pattern which you have tried with so far. The best I can come up with is,
str_text='your text here containing DESTRUCTORS'
match=re.search('pass all the destructors combination here', str_text, flags=re.IGNORECASE)
Try for more patterns available for string formatting with regex here,https://docs.python.org/3/library/re.html
From text transcripts, I want to capture all names of speakers.
The target names start at the start of a line and should end at a ": " (ie. colon and space).
Optionally, for even finer control, it may be safe to assume the first colon and two spaces.
Example text:
Julian Z.: What's really exciting is the opportunity to be more intelligent about how you approach trying to reach your consumer. In a world where digital and the use of digital has exploded, to be able to have one-on-one conversations in the digital world, and to be able to eventually translate that into the TV space, whether that be addressable or data-driven, is really fantastic. Because at the end of the day, you want your brand, in our case, our networks, to be able to have a relationship with the consumer. Data is a proxy to allow for that to occur.
From an advertiser perspective, obviously now the ability to go to the broadcast networks and have a data-driven buy has absolutely blown up and proliferated. That's with us. That's with some of our competitors. Obviously, we think we're the best at it, but neither here nor there. I think it's a really wonderful foundational approach for advertisers to take. I think it's a great advancement in the market.
As a spender of money, and as somebody who is trying to get people to engage with our brands, the ability to use data to really have, again, these really one-on-one, unique conversations, and to be able to deliver creative content that's relevant for individual consumers, that's driven by what we know about the consumer, now, ultimately, where we can reach them effectively and in environments where we know they're engaged, is really a great, tremendous advancement. You'll see by our ratings numbers, which are on the upswing, that approach has really had a direct impact on what our linear ratings have resulted in.
Speaker 2: Great. Tell us a little bit about Viacom. It's a lot of fans, a lot of passion in people. How do you define the audience in broad strokes? How do they respond to advertising and what are some of the concerns that consumers have around ads?
Julian Z.: Well, I think, again, when you're talking about how we're reaching fans, it is using intelligence, and information, and data, not only to profile who our fans are, but ultimately where they're best reached. Our job is to deliver great, compelling content, which we believe we're really, really good at.
In order to do that, there's the linear side of the equation, but of course we want to make sure that we're reaching our fans in digital as well, and that there's a 360 kind of fan experience. We believe holistically that our fans are really the base of what we're trying to do. We're trying to please and create value for our fans. The more we engage with them, and the more we know about them, the better we're able to deliver customized content that fits their need.
Ultimately, as a content creator, what's more exciting than to delivery really great content to people that they really, really engage with and they build relationships with? That's all you can really hope for is, somebody that creates content, is to be able to develop compelling content and content that your audience really wants to engage with.
Speaker 2: When you look at targeting, is that a cross-platform? Where does that targeting happen?
Julian Z.: It absolutely is cross-platform. Of course, there is natural addressability in the digital market, because it is much more of a one-to-one. But now you see a lot of the MVPDs have obviously opened up addressable inventory. A lot of the MVPDs now have matured their addressable footprint, which allows you now to have a digital-like, not exactly the same obviously, but a digital-like experience in the linear space, to deliver content to the consumer or advertising to the consumer when it's relevant and when it's going to have the most impact for your message.
Ultimately, it's absolutely cross-platform because addressability is all about having that conversation, having that direct one-to-one with your audience. Our partners on the MVPD side have really matured over the last several years as of regard to addressable, and now you can have that 360 experience of having a conversation in linear and in digital that really is addressable.
Example strings to be captured are: Julian Z. and Speaker 2. Names will vary from text to text. I need all/multiple names present. As you see, names may include a mixture of alpha case, punctuation characters and numbers.
I will want to deduplicate names, which are repeated in the text, but believe I should shelve that for now, focusing this question on the capture.
I have tried plenty, for the last day or two.
eg. ^[^:]+\s* with /g comes close, but only captures the first, single Julian Z., whereas I want everything. For now, I am out of ideas and need to learn how to do this.
Regex to match any characters up until the first colon:
/^.*?(?=:)/gm
https://regex101.com/r/3uyXMM/3
^: match from beginning of line
.: match anything
*?: non-greedy search, so it stops at first colon (see next line)
(?=:): positive lookahead meaning next character should be colon but it doesn't capture
g: don't return after first match, returns all matches
m: run regex for each line
You can use this regex based on a negated character class:
/^\w[^:\n]*/mg
RegEx Demo 1
RegEx Demo 2
RegEx Breakup:
^\w: Match a word character at the start
[^:\n]*: Match zero or more of any character that is not a colon and not a newline.
Code:
var names = inputData.transcript.match(/^\w[^:\n]*/mg) || [];
How can I best or better figure out the topic, point, or subject of a Natural Language sentence with Clojure or Clojure Script?
Currently, I am using a PoS tagger and taking the nouns as the subject and if there is an adjective before it then that too.
However, this method doesn't always work. For example, it doesn't work when the subject is not a noun.
It seems hard to detect a sentence boundary in a text. Quotation marks like .!? may be used to delimite sentences but not so accurate as there may be ambiguous words and quotations such as U.S.A or Prof. or Dr. I am studying Tperlregex library and Regular Expression Cookbook by Jan Goyvaerts but I do not know how to write the expression that detects sentence?
What may be comparatively accurate expression using Tperlregex in delphi?
Thanks
First, you probably need to arrive at your own definition of what a "sentence" is, then implement that definition. For example, how about:
He said: "It's OK!"
Is it one sentence or two? A general answer is irrelevant. Decide whether you want it to interpret it as one or two sentences, and proceed accordingly.
Second, I don't think I'd be using regular expressions for this. Instead, I would scan each character and try to detect sequences. A period by itself may not be enough to delimit a sentence, but a period followed by whitespace or carriage return (or end of string) probably does. This immediately lets you weed out U.S.A (periods not followed by whitespace).
For common abbreviations like Prof. an Dr. it may be a good idea to create a dictionary - perhaps editable by your users, since each language will have its own set of common abbreviations.
Each language will have its own set of punctuation rules too, which may affect how you interpret punctuation characters. For example, English tends to put a period inside the parentheses (like this.) while Polish does the opposite (like this). The same difference will apply to double quotes, single quotes (some languages don't use them at all, sometimes they are indistinguishable from apostrophes etc.). Your rules may well have to be language-specific, at least in part.
In the end, you may approximate the human way of delimiting sentences, but there will always be cases that can throw the analysis off. For example, assuming that you have a dictionary that recognizes "Prof." as an abbreviation, what are you going to do about
Most people called him Professor Jones, but to me he was simply The Prof.
Even if you have another sentence that follows and starts with a capital letter, that still won't help you know where the sentence ends, because it might as well be
Most people called him Professor Jones, but to me he was simply Prof. Bill.
Check my tutorial here http://code.google.com/p/graph-expression/wiki/SentenceSplitting. This concrete example can be easily rewritten to regular expressions and some imperative code.
It will be wise to use a NLP processor with a pre-trained model. EnglishSD.nbin is one such model that is available for OpenNLP and it can be used in Visual Studio with SharpNLP.
The advantage of using this method is numerous. For example consider the input
Prof. Jessica is a wonderful woman. She is a native of U.S.A. She is married to Mr. Jacob Jr.
If you are using a regex split, for example
string[] sentences = Regex.Split(text, #"(?<=['""A-Za-z0-9][\.\!\?])\s+(?=[A-Z])");
Then the above input will be split as
Prof.
Jessica is a wonderful woman.
She is a native of U.
S.
A.
She is married to Mr.
Jacob Jr.
However the desired output is
Prof. Jessica is a wonderful woman.
She is a native of U.S.A. She is married to Mr. Jacob Jr.
This kind of logical sentence split can be achieved only using trained models from OpenNLP project. The method is as simple as this.
private string mModelPath = #"C:\Users\ATS\Documents\Visual Studio 2012\Projects\Google_page_speed_json\Google_page_speed_json\bin\Release\";
private OpenNLP.Tools.SentenceDetect.MaximumEntropySentenceDetector mSentenceDetector;
private string[] SplitSentences(string paragraph)
{
if (mSentenceDetector == null)
{
mSentenceDetector = new OpenNLP.Tools.SentenceDetect.EnglishMaximumEntropySentenceDetector(mModelPath + "EnglishSD.nbin");
}
return mSentenceDetector.SentenceDetect(paragraph);
}
where mModelPath is the path of the directory containing the nbin file.
The mSentenceDetector is derived from the OpenNLP dll.
You can get the desired output by
string[] sentences = SplitSentences(text);
Kindly read through this article I have written for integrating SharpNLP with your Application in Visual Studio to make use of the NLP tools
I'm working on a web application that parses and displays email messages in a threaded format (among other things). Emails may come from any number of different mail clients, and in either text or HTML format.
Given that most people have a tendency to top post, I'd like to be able to hide the duplicated message in an email reply in a manner similar to how Gmail does it (e.g. "show quoted text").
Determining which part of the message is the reply is somewhat challenging. Personally, I use "> " delimiters at the beginning of the quoted text when replying. I created a regexp that looks for these lines and wraps a div around them to allow some JS to hide or show this block of text.
I then noticed that Outlook doesn't use the "> " characters by default, it simply adds a header block above the reply with the summary of the headers (From, Subject, Date, etc.). The reply is untouched. I can match on this and hide the rest of the email, working with the assumption that it's a top quote.
I then looked at Thunderbird, and it uses "> " for text, and <blockquote> for HTML mails. I still haven't looked at what Apple Mail does, what Notes does, or what any of the other millions of mail clients out there do.
Will I be writing a special case regexp for every single client out there? or is there something I'm missing?
Any suggestions, sample code or pointers to third party libraries much appreciated!
It'll be pretty hard to duplicate the way gmail does it since it doesn't care about whether it was a quoted piece or not, like Zac says, it just seems to care about the diff.
Its actually pretty hard to get this right 100% of the time. Plain text email is "lossy", its entirely possible for you to send
> Here is my long line that is over 74 chars (email line length limit)
Which can get encoded as something like
> Here is my long line that is over 74 chars (email=
line length limit)
And then is decoded as
> Here is my long line that is over 74 chars (email
line length limit)
Making it indistinguishable from an inline-reply.
This is email, so variations are abound. Email usually line-wraps at something like 74 characters, and encoding schemes can differ. Its a real PITA. If you can access the HTML version, you will probably have better luck looking for quote tags and the like. Another idea would be to parse both the plain text and html version to try and determine the boundries.
Additionally, its best to just plan for specific client hacks. They all construct mime messages differently, both in structure and header content.
Edit: I say this with the experience of writing an email processing system as well as seeing several people try to do the -exact- thing you're doing. It always only got "ok" results.
From what I can tell, gmail does not bother about prefixed lines or section headings, except to ignore them. If the text lines appeared earlier in the thread, and then reappear, it is considered to be quoted. Thus, e.g., if you send multiple messages and don't change your signature, the signature is considered to be quoted. If you've already dealt with the '>' prefix, a simple diff should do most of the rest. No need to get fancy.
First thing I think I'd do is strip out all the white space, or reduce white space to 1 between each word, and special characters from both blocks, then look for the old one in the new one.
Here's a mozdev project that may be helpful for others who stumble across this page looking for a Thunderbird solution:
http://quotecollapse.mozdev.org/