Update usage.rst

fix grammar mistakes

Author: evelinamorim

docs/source/usage.rst

@@ -2,12 +2,12 @@ Usage
 =====
 
 
-Getting Start
+Getting Started
 -----
 
-After installation, you will be able to import and create an object of type Narrative. In this object,
+After installation, you will be able to import and create an object of the Narrative type. In this object,
 the text2story package will perform all automatic annotations. In the next section, we cover all the
-functionalities about the annotators.
+functionalities of the annotators.
 
 The Narrative Object
 -----
@@ -19,34 +19,34 @@ for English, 'pt' for Portuguese, and so on.), the text of the narrative (the le
 to the models applied in the pipeline), and the document creation date. The last one is especially important for news
 stories which usually present a publication date.
 
-The code bellow presents an example with a raw text English that is used to create a Narrative object.
+The code below presents an example with a raw text English that is used to create a Narrative object.
 
 .. literalinclude:: examples/narrative_object.py
   :language: python
 
 
 The narrative object is used then to process all the pipeline of annotators that will extract the narrative components.
-The Section Annotators Module details how to build such pipeline.
+The Section Annotators Module details how to build such a pipeline.
 
 The Readers Module
 -----
 
-If the user want to read an already human annotated dataset, it is possible to do such a thing using some object of the
+If the user wants to read an already human-annotated dataset, it is possible to do such a thing using some object of the
 type reader. text2story readers module supports the following formats: ACE, BRAT, CSV, ECB, Framenet, Propbank.
-Each one of this module inherits the methods from the abstract class `Read`, which obliges all inherited classes to
+Each one of these modules inherits the methods from the abstract class `Read`, which obliges all inherited classes to
 implement the method `process` and `process_file`. The first method reads all files (text and annotations) from a
 given directory, and the second reads only one file (text and its annotations).
 
-It is assumed that both methods returns a list of `TokenCorpus`, which is type that contains a token and its
-annotations, if they exists. This is also a class defined in readers module.
+It is assumed that both methods return a list of `TokenCorpus`, which is the type that contains a token and its
+annotations if they exist. This is also a class defined in the reader's module.
 
 Next, a code example to read a directory with annotations in BRAT format.
 
 .. literalinclude:: examples/read_brat_dir.py
   :language: python
 
 
-The next code illustrate how to use `ReadBrat` to read only one file.
+The next code illustrates how to use `ReadBrat` to read only one file.
 
 .. literalinclude:: examples/read_brat_file.py
   :language: python
@@ -55,26 +55,26 @@ The next code illustrate how to use `ReadBrat` to read only one file.
 The Annotators Module
 -----
 
-There are two type of annotators in the text2tstory: the native ones and the customized ones.
-The first is composed by a set of pre-trained models that are part of the library, and
-are all naturally integrated in our pipeline. The second type is composed by annotators that
-anyone can built and integrate in our pipeline. For both, it is required to load the models for the
-language of the used examples. The code bellow is used to load the models for the English language.
+There are two types of annotators in the text2tstory: the native ones and the customized ones.
+The first is composed of a set of pre-trained models that are part of the library, and
+are all naturally integrated in our pipeline. The second type is composed of annotators that
+anyone can build and integrate into our pipeline. For both, the models must be loaded in the
+language of the examples used. The code below is used to load the models for the English language.
 
 .. literalinclude:: examples/load_models.py
   :language: python
 
 
 .. note::
 
-   Before load models, it is required to install the model for tei2go. For instance, if you are going to use english models. You should execute `pip install https://huggingface.co/hugosousa/en_tei2go/resolve/main/en_tei2go-any-py3-none-any.whl`.
+   Before loading models, the model for tei2go must be installed. For instance, if you are going to use English models. You should execute `pip install https://huggingface.co/hugosousa/en_tei2go/resolve/main/en_tei2go-any-py3-none-any.whl`.
 
 Next, we describe how the native and custom annotators work.
 
 Native Annotators
 ^^^^^^^^^^^^^^^^^
 
-The native annotators are the following modules: NLTK, PY_HEIDELTIME, BERTNERPT, TEI2GO, SPACY and ALLENNLP. Next, we detail
+The native annotators are the following modules: NLTK, PY_HEIDELTIME, BERTNERPT, TEI2GO, SPACY, and ALLENNLP. Next, we detail
 the usage of each one of these annotators.
 
 Participants
@@ -83,8 +83,8 @@ For  participants, we have the following annotators SPACY ('pt','en'), NLTK ('en
 BERTNERPT ('pt'), and SRL ('pt').
 
 The NLTK module uses a Named Entity Recognition (NER) model trained in the ACE dataset to identify participants in
-the English language. So, after loading the english models, you can use the code bellow to extract
-participants using the NLTK module. Others modules that employs NER to identify participants are SPACY
+the English language. So, after loading the English models, you can use the code below to extract
+participants using the NLTK module. Other modules that employ NER to identify participants are SPACY
 (en_core_web_lg/'en', pt_core_news_lg/'pt') and BERTNERPT (https://huggingface.co/arubenruben/NER-PT-BERT-CRF-Conll2003).
 Bellow, an example of using only NLTK to extract participants from a narrative.
 
@@ -106,7 +106,7 @@ Time
 ''''''''
 
 For time expression, text2story has py_heideltime and tei2go to identify time expressions both in Portuguese and
-English languages. The code is similar to the extraction of participants. See the example bellow:
+English languages. The code is similar to the extraction of participants. See the example below:
 
 
 .. literalinclude:: examples/extract_time.py