CogComp · danyaljj · Feb 21, 2017 · Feb 21, 2017
diff --git a/lbjava/README.md b/lbjava/README.md
@@ -11,7 +11,7 @@ leaving him to reason more directly about his application.
 Visit each link for its content 
  1. [Introduction](doc/INTRO.md) 
  2. [Basics and definitions](doc/DEFINITIONS.md)
- 3. [A working example: classifiying newsgroup documents into topics](doc/20NEWSGROUP.md)
+ 3. [A working example: classifying newsgroup documents into topics](doc/20NEWSGROUP.md)
  4. [Syntax of LBJava](doc/LBJLANGUAGE.md)
  5. [LBJava library](doc/LBJLIBRARY.md)
  6. [A working example: regression](doc/REGRESSION.md)
@@ -80,7 +80,7 @@ NB: If you need to run `mvn clean` at any point, make sure to create `target/cla
 running `mvn compile/install` since it is required for the java-source compilation process.
 
 ## Using ILP inference 
-LBJava uses the the solvers included in [illinois-inference](https://gitlab-beta.engr.illinois.edu/cogcomp/inference/) for 
+LBJava uses the solvers included in [illinois-inference](https://gitlab-beta.engr.illinois.edu/cogcomp/inference/) for 
 inference. We refer the interested reader to the aforementioned library, 
 for more details and instructions on how to install these libraries. 
 

diff --git a/lbjava/doc/20NEWSGROUP.md b/lbjava/doc/20NEWSGROUP.md
@@ -74,7 +74,7 @@ in the following snippet, simply extends it to
 take advantage of that functionality; it won't be necessary to override `reset()`
 or `close()`. `NewsgroupParser` takes as input a file
 containing the names of other files, assuming that each of those files
-represents a single newgroup post.  For brevity, we have hidden in `Post`'s 
+represents a single newsgroup post.  For brevity, we have hidden in `Post`'s 
 constructor the code that actually does the work of filling the fields
 of a `Post` object.
 
@@ -540,7 +540,7 @@ At the bottom of the table will always be the overall accuracy of the classifier
  <img width="503" alt="screen shot 2015-11-17 at 3 46 18 am" src="https://cloud.githubusercontent.com/assets/2441454/11207928/d9eb9f3a-8cdd-11e5-8f34-989f3ddebd78.png">
 
  The `TestDiscrete` class also supports the notion of a null label, which is a label intended to
- represent the absense of a prediction. The 20 Newsgroups task doesn’t make use of this concept,
+ represent the absence of a prediction. The 20 Newsgroups task doesn’t make use of this concept,
  but if our task were, e.g., named entity classification in which every phrase is potentially a named
  entity, then the classifier will likely output a prediction we interpret as meaning “this phrase is
  not a named entity.” In that case, we will also be interested in overall precision, recall, and F1

diff --git a/lbjava/doc/LBJLANGUAGE.md b/lbjava/doc/LBJLANGUAGE.md
@@ -225,7 +225,7 @@ classifier expressions, the cast propagates down to those classifier expressions
 A conjunction is written with the double ampersand operator (`&&`) in between two classifier
 expressions (see Figure 4.1 for an example). The conjunction of two classifiers results in a new
 classifier that combines the values of the features returned by its argument classifiers. The nature
-of the combination depends on the feature return types of the argument classifiers. Table bellow
+of the combination depends on the feature return types of the argument classifiers. Table below
 enumerates all possibilities and gives the feature return type of the resulting conjunctive classifier.
 
 
@@ -324,7 +324,7 @@ subsets (folds). If the `split-strategy` argument is not provided, the default v
  i.e. `[ — 1 — | — 2 — | ... | — K — ]`
 
  - `kth` - The `kth` split strategy also attempts to partition the set of examples in to `K` equally
-   sized subsets with a round-robin style assignement scheme. The `x`’th example encountered 
+   sized subsets with a round-robin style assignment scheme. The `x`’th example encountered 
     is assigned to the `(x%K)`’th subset.
     i.e. `[ 1 2 3 4 ... K 1 2 3 4 ... K ... ]`
 
@@ -738,4 +738,4 @@ p.thinkness = {{ 1 -> 3 : 0.5}};
 Cross validation is useful, and essential to avoid overfitting problem. For k-fold cross validation, the syntax is: 
 ```
 cval k "random"
-```
+```
diff --git a/lbjava/doc/LBJLIBRARY.md b/lbjava/doc/LBJLIBRARY.md
@@ -174,7 +174,7 @@ as an interface between learning classifiers defined in an  source file and appl
 make on-line use of their learning capabilities. The class generated by the  compiler when
 translating a learning classifier expression will always indirectly extend this class.
 
-In addition to the methods inherited from `Classifier`, this class defines the following nonstatic,
+In addition to the methods inherited from `Classifier`, this class defines the following non-static,
 learning related methods. These are not the only methods defined in class `Learner`, and
 advanced users may be interested in perusing the Javadoc for descriptions of other methods.
 

diff --git a/lbjava/doc/REGRESSION.md b/lbjava/doc/REGRESSION.md
@@ -4,7 +4,7 @@ title: REGRESSION
 
 # 6. A working example: Regression
 
-As mentioned in [Section 2 Basics and definitions](DEFINITION.md#feature), there are two feature types in LBJava: `discrete` and `real`. In machine learning, classification refers to the problem of predicting the class of unlabeled data for which the output type is `discrete`. On the hther hand, regression refers to the problem that the desired output is continuous or `real`. [Section 3 A working example: classifying newsgroup documents into topics](20NEWSGROUP.md) gives an example of how to use LBJava for `discrete` type and this tutorial is dedicated to `real` type.
+As mentioned in [Section 2 Basics and definitions](DEFINITION.md#feature), there are two feature types in LBJava: `discrete` and `real`. In machine learning, classification refers to the problem of predicting the class of unlabeled data for which the output type is `discrete`. On the other hand, regression refers to the problem that the desired output is continuous or `real`. [Section 3 A working example: classifying newsgroup documents into topics](20NEWSGROUP.md) gives an example of how to use LBJava for `discrete` type and this tutorial is dedicated to `real` type.
 
 ## 6.1 Setting Up