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Which one among the following NLP topics will be easier to work with?
Question answering
Paraphrase detection
Short text conversation
Author identification
The final one, Author identification. You don't need to have any understanding of the language you are dealing with, which the first three presuppose.
There is already a lot of literature on the topic; generally you identify features in texts, and map these onto a set of authors' known features. This can easily be done with cluster analysis or Machine Learning. So, it's not actually as NLP-heavy as the others.
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I read several posts(here and here) online about LDA topic modeling. All of them only use uni-grams. I would like to know why bi-grams and tri-grams are not used for LDA topic modeling?
It's a matter of scale. If you have 1000 types (ie "dictionary words"), you might end up (in the worst case, which is not going to happen) with 1,000,000 bigrams, and 1,000,000,000 trigrams. These numbers are hard to manage, especially as you will have a lot more types in a realistic text.
The gains in accuracy/performance don't outweigh the computational cost here.
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How to represent a system like google docs queuing system using UML class diagrams? I haven't used much UML and trying to design a system like Google docs.
I'm not a UML pro but I took a quick stab at it. There's probably some missing pieces here but I think it's a good start. It would be great if someone who knows how to correctly model this can provide their diagram.
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No other game impressed me more than ADOM because of its almost endless ways you can interact with its world. To name a few: If you have waterproof blankets your items will not rust in rain, you can cut trees to build a bridge, dip weapons into potions, kick locked doors etc.
With such complexity of interactions each new feature may require refactoring, recompilation, then cause broken tests, bugs etc. This seems to be an interesting case of the Expression Problem.
The Question:
Can solution to Expression Problem be expressed in Agda or Haskell as a reusable library or a design pattern?
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I'm learning Haskell in order to gain knowledge of Functional programming to apply to Java 8. Is Haskell a marketable skill?
Haskell is used "in the real world," but in terms of "Am I likely to get a job using this?" it's on the very low end. Almost any other language you can likely name has more jobs that require it.
But in terms of learning, Haskell is a great language. It really helps you think about your programs differently. And having a good mind for application architecture is a very marketable skill.
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What are applications where search techniques or more specifically planning techniques are used? I am most interested in examples in use.
I know that A* is used for path planning in Robotics, that planning is used in logistics (details would be great) but what other usages are there?
For Search in general Google, etc come to mind with their inverted indices. Again, where else is it used?
For planning examples, including logistics challenges, take a look at this list. Each use case comes with multiple datasets and a problem definition.