Answer:
Implementing on Python for the question, the following is the code.
Explanation:
def intialMatch(l):
word_dict={}
for word in l.split():
if word_dict.get(word)==None:
word_dict[word]=[]
for key in word_dict.keys():
if key[0]==word[0] and (word is not key) :
values = word_dict.get(key)
if word not in values:
values.append(word)
for key,values in word_dict.items():
for value in values:
if value==key:values.remove(value)
return word_dict
t='do what you can with what you have'
print(intialMatch(t))
Answer:
The most basic and useful technique in NLP is extracting the entities in the text. It highlights the fundamental concepts and references in the text. Named entity recognition (NER) identifies entities such as people, locations, organizations, dates, etc. from the text.
Answer:
core engine or system software.
Explanation:
just because
Answer:
online help and user forums iam not sure of this amswer maybe
We can define a word as a group of characters without a space between them. To find the words of the input string , w can use split(delimiter) which returns a list of strings which had the defined delimiter between them in the input string.
def countWords(string):
words = string.split(" ")
count = len(words)
return count
Here we set the delimiter as the space character, and returned the length of the words list. I split each step into its own line for readability, however the function could be one line:
return len(string.split())
Here, no delimiter is specified. If one isn't given, it will default to split at any whitespace, including space.
