Many journals require submission of a blinded version of your paper. The blinded version usually removes:
the list of authors
any citations to the authors' work
How can I create a blinded version of my manuscript without doing this manually?
You could use the extract package to generate a separate LaTeX source file for the paper with the author list and acknowledgements automatically removed. I believe the versions package could be used to do this as well.
As for blinding the citations, I think the point is to refer to your own work in a neutral and unrevealing way. Here are some examples of correct and incorrect ways to cite your own work in a blinded manuscript:
Incorrect: "As I have argued elsewhere..."
Corrected: "As Jones (2001) has argued..."
Incorrect: "As I argue in (Jones 2001)."
Corrected: "As Jones (2001) argues."
Incorrect: "This argument is fleshed out in my (2001)."
Corrected: "Jones (2001) makes this argument in more detail."
One thing that is often overlooked in these cases, is that you do not really need to remove citations of your own work, as long as you refer to them in the third person. Instead of writing "As shown in our previous paper (Removed for review) ...", you should write "As shown by Smith et al. (Smith, Jones, and Adams, 2008)...".
This reads much better and saves you some of the trouble, while typically satisfying the submission rules for journals and conference. This way all you have to do is remove the list of authors, which is not hard to do by hand.
Hadley, you say it's necessary to remove all references to your own work. Is that really the case? At a certain career stage, the absence of a set of citations could very well give away the identity of the author!
I will betray my old-fart status by advising you to ignore those who advise using the active voice. Changing "Hadley (2009) showed that the sky is blue" to "The sky is blue (Hadley 2009)" is a simple solution to the problem of "I" (and "we" and "some of us" and "some of us, plus others") in technical writing. And it has an additional bonus: it focusses attention on the matter at hand, not on the people involved. You want the reader's internal eye to see that broad sweep of azure, not your avatar ;-)
To remove the list of authors, you could use \renewcommand to "edit" your \maketitle in order to avoid listing article's authors. For author's citations, you could do the same.
For example.
\renewcommand{\maketitle}{\title}
Related
I saw an acronym for what makes a "good" JIRA ticket on wikipedia that I can't find it anymore.
It was something along the lines of quantifiable, actionable, defined, etc. The acronym did not actually spell out "JIRA."
Does anyone have a reference to the acronym I refer to? I would appreciate it.
Most likely, you mean the acronym S.M.A.R.T..
The single letters mean:
Specific
Measurable
Achievable
Relevant
Time-bound
For more information, here is link to Wikipedia:
https://en.m.wikipedia.org/wiki/SMART_criteria
During natural language processing, what should be the logic to determine if the current sentence has "cues" which needs to be taken from the previous sentence while parsing.
I am not sure how to aptly describe this issue or what sub-topic of NLP it is referred as, so I would present an example.
If previous sentence was Find me an Italian restaurant in New York city
and the current sentence is What's the weather there tomorrow
How should the parser proceed to understand that there in the current sentence refers to New york which was mentioned in the previous sentence.
PS - I am fairly new to NLP, so please be kind here with your explanations.
An anaphor is an expression that refers back to a previous expression in a natural language discourse. For example: "Mary died. She was very old." The word she refers to Mary, and is described as an anaphoric reference to Mary. Mary is described as the antecedent of she. Anaphoric references are frequently pronouns, as in the example, but may also be definite noun phrases, as in: "Ronald Reagan frowned. The President was clearly worried by this issue." Here The President is an anaphoric reference to Ronald Reagan. Anaphors may in some cases not be explicitly mentioned in a previous sentence - as in "John got out his pencil. He found that the lead was broken." The lead here refers to a subpart of his pencil. Anaphors need not be in the immediately preceding sentence, they could be further back, or in the same sentence, as in "John got out his pencil, but found that the lead was broken." In all our examples so far the anaphor and the antecedent are noun phrases, but VP and sentence-anaphora is also possible, as in "I have today dismissed the prime minister. It was my duty in the circumstances." Here It is an anaphoric reference to the VP dismissed the prime minister.
For a fairly complete and quite entertaining treatment of anaphora, see Hirst, G. Anaphora in Natural Language Understanding: A Survey Springer Lecture Notes in Computer Science 119, Berlin: Springer, 1981.
You can also find an algorithm to solve the problem in this Paper.
Is there a way to generate a one-sentence summarization of Q&A pairs?
For example, provided:
Q: What is the color of the car?
A: Red
I want to generate a summary as
The color of the car is red
Or, given
Q: Are you a man?
A: Yes
to
Yes, I am a man.
which accounts for both question and answer.
What would be some of the most reasonable ways to do this?
I had to once work on solving the opposite problem, i.e. generating questions out of sentences from Wikipedia articles.
I used the Stanford Parser to generate parse trees out of all possible sentences in my training dataset.
e.g.
Go to http://nlp.stanford.edu:8080/parser/index.jsp
Enter "The color of the car is red." and click "Parse".
Then look at the Parse section of the response. The first layer of that sentence is NP VP (noun phrase followed by a verb phrase).
The second layer is NP PP VBZ ADJP.
I basically collected these patterns across 1000s of sentences, sorted them how common each patter was, and then used figured out how to best modify this parse tree to convert into each sentence in a different Wh-question (What, Who, When, Where, Why, etc)
You could you easily do something very similar. Study the parse trees of all of your training data, and figure out what patterns you could extract to get your work done. In many cases, just replacing the Wh word from the question with the answer would give you a valid albeit somewhat awkwardly phrases sentence.
e.g. "Red is the color of the car."
In the case of questions like "Are you a man?" (i.e. primary verb is something like 'are', 'can', 'should', etc), swapping the first 2 words usually does the trick - "You are a man?"
I don't know any NLP task that explicitly handles your requirement.
Broadly, there are two kinds of questions. Questions that expect a passage as the answer such as definition or explain sort: What is Ebola Fever. The second type are fill in the blank which are referred to as Factoid Questions in the literature such as What is the height of Mt. Everest?. It is not clear what kind of question you would like to summarize. I am assuming you are interested in factoid questions as your examples refer to only them.
A very similar problem arises in the task of Question Answering. One of the first stages of this task is to generate query. In the paper: An Exploration of the Principles Underlying
Redundancy-Based Factoid Question
Answering; Jimmy Lin 2007, the author claims that better performance can be achieved by reformulating the query (see section 4.1) to the form more likely to appear in free text. Let me copy some of the examples discussed in the paper.
1. What year did Alaska became a state?
2. Alaska became a state ?x
1. Who was the first person to run the miles in less than four minutes?
2. The first person to run the miles in less than four minutes was ?x
In the above examples, the query in 1 is reformulated to 2. As you might have already observed, ?x is the blank that should be filled by the answer. This reformulation is carried out through a dozen hand-written rules and are built into the software tool discussed in the paper: ARANEA. All you have to do is to find the tool and use it, the paper is a good ten years old, I cannot promise you anything though :)
Hope this helps.
I'm not hoping for clues on picking key words, there are guides about that already.
I'm hoping to get a decisive idea, with a reference to documentation or statements from Apple of:
The correct keyword list syntax.
How they are employed for matching in Apple's back end.
For example:
Should they be comma delineated: "ham,chips,beans"?
Or space delineated: "ham chips beans"?
Or comma and space: "ham, chips, beans"?
If customers might search for me by a phrase, such as "hungry cat", should I include "hungry, cat, hungry cat, hungry-cat"? Or is "hungry cat" sufficient?
I believe it's not necessary to add plural forms: "cats" isn't needed, provided I have "cat". But what about other stemming? If people search for "eating cats", is "eat, cat" in my search terms enough?
Thanks.
There are two votes to close stating this question is "opinion based". I've adjusted the question to make it clear that I am not looking for opinion, but statements or documentation from Apple.
I need to parse recipe ingredients into amount, measurement, item, and description as applicable to the line, such as 1 cup flour, the peel of 2 lemons and 1 cup packed brown sugar etc. What would be the best way of doing this? I am interested in using python for the project so I am assuming using the nltk is the best bet but I am open to other languages.
I actually do this for my website, which is now part of an open source project for others to use.
I wrote a blog post on my techniques, enjoy!
http://blog.kitchenpc.com/2011/07/06/chef-watson/
The New York Times faced this problem when they were parsing their recipe archive. They used an NLP technique called linear-chain condition random field (CRF). This blog post provides a good overview:
"Extracting Structured Data From Recipes Using Conditional Random Fields"
They open-sourced their code, but quickly abandoned it. I maintain the most up-to-date version of it and I wrote a bit about how I modernized it.
If you're looking for a ready-made solution, several companies offer ingredient parsing as a service:
Zestful (full disclosure: I'm the author)
Spoonacular
Edamam
I guess this is a few years out, but I was thinking of doing something similar myself and came across this, so thought I might have a stab at it in case it is useful to anyone else in f
Even though you say you want to parse free test, most recipes have a pretty standard format for their recipe lists: each ingredient is on a separate line, exact sentence structure is rarely all that important. The range of vocab is relatively small as well.
One way might be to check each line for words which might be nouns and words/symbols which express quantities. I think WordNet may help with seeing if a word is likely to be a noun or not, but I've not used it before myself. Alternatively, you could use http://en.wikibooks.org/wiki/Cookbook:Ingredients as a word list, though again, I wouldn't know exactly how comprehensive it is.
The other part is to recognise quantities. These come in a few different forms, but few enough that you could probably create a list of keywords. In particular, make sure you have good error reporting. If the program can't fully parse a line, get it to report back to you what that line is, along with what it has/hasn't recognised so you can adjust your keyword lists accordingly.
Aaanyway, I'm not guaranteeing any of this will work (and it's almost certain not to be 100% reliable) but that's how I'd start to approach the problem
This is an incomplete answer, but you're looking at writing up a free-text parser, which as you know, is non-trivial :)
Some ways to cheat, using knowledge specific to cooking:
Construct lists of words for the "adjectives" and "verbs", and filter against them
measurement units form a closed set, using words and abbreviations like {L., c, cup, t, dash}
instructions -- cut, dice, cook, peel. Things that come after this are almost certain to be ingredients
Remember that you're mostly looking for nouns, and you can take a labeled list of non-nouns (from WordNet, for example) and filter against them.
If you're more ambitious, you can look in the NLTK Book at the chapter on parsers.
Good luck! This sounds like a mostly doable project!
Can you be more specific what your input is? If you just have input like this:
1 cup flour
2 lemon peels
1 cup packed brown sugar
It won't be too hard to parse it without using any NLP at all.