Doris doc (apache#31099)

docs/en/docs/data-operate/import/import-way/s3-load-manual.md

@@ -95,7 +95,7 @@ example:
 
 <version since="1.2">
 
-2. Support using temporary security credentials to access object stores that support the S3 protocol:
+2. Support using temporary security credentials to access object storage that support the S3 protocol:
 
 ```
   WITH S3

docs/en/docs/data-table/best-practice.md

@@ -113,7 +113,7 @@ In business, most users will choose to partition on time, which has the followin
 
 1.3.1.2. List Partitioning
 
-In business,, users can select cities or other enumeration values for partition.
+In business, users can select cities or other enumeration values for partition.
 
 1.3.2. Hash Bucketing
 
@@ -179,4 +179,4 @@ Users can modify the Schema of an existing table through the Schema Change opera
 - Reorder columns
 - Adding or removing index
 
-For details, please refer to [Schema Change](../advanced/alter-table/schema-change.md)
+For details, please refer to [Schema Change](../advanced/alter-table/schema-change.md)

docs/en/docs/data-table/index/inverted-index.md

@@ -35,7 +35,7 @@ From version 2.0.0, Doris implemented inverted index to support fulltext search
 
 ## Glossary
 
-- [inverted index](https://en.wikipedia.org/wiki/Inverted_index) is a index techlogy used in information retirval commonly. It split text into word terms and construct a term to doc index. This index is called inverted index and can be used to find the docs where a specific term appears.
+- [inverted index](https://en.wikipedia.org/wiki/Inverted_index) is a index techlogy used in information retrieval commonly. It split text into word terms and construct a term to doc index. This index is called inverted index and can be used to find the docs where a specific term appears.
 
 
 ## Basic Principles
@@ -353,7 +353,7 @@ mysql> SELECT count() FROM hackernews_1m WHERE comment MATCH_ANY 'OLAP';
 1 row in set (0.02 sec)
 ```
 
-- Semilarly, count on 'OLTP' shows 0.07s vs 0.01s. Due to the cache in Doris, both LIKE and MATCH_ANY is faster, but there is still 7x speedup.
+- Similarly, count on 'OLTP' shows 0.07s vs 0.01s. Due to the cache in Doris, both LIKE and MATCH_ANY is faster, but there is still 7x speedup.
 ```sql
 mysql> SELECT count() FROM hackernews_1m WHERE comment LIKE '%OLTP%';
 +---------+
@@ -394,7 +394,7 @@ mysql> SELECT count() FROM hackernews_1m WHERE comment MATCH_ALL 'OLAP OLTP';
 ```
 
 - search for at least one of 'OLAP' or 'OLTP', 0.12s vs 0.01s，12x speedup
-  - using MATCH_ALL if you only need at least one of the keywords appears
+  - using MATCH_ANY if you only need at least one of the keywords appears
 ```sql
 mysql> SELECT count() FROM hackernews_1m WHERE comment LIKE '%OLAP%' OR comment LIKE '%OLTP%';
 +---------+