[typo] Fix incorrect spelling for "corpus" (#3154)

examples/applications/semantic-search/README.md

@@ -124,7 +124,7 @@ We list a handful of common use cases:
 [semantic_search_quora_pytorch.py](semantic_search_quora_pytorch.py) [ [Colab version](https://colab.research.google.com/drive/12cn5Oo0v3HfQQ8Tv6-ukgxXSmT3zl35A?usp=sharing) ] shows an example based on the [Quora duplicate questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset. The user can enter a question, and the code retrieves the most similar questions from the dataset using `util.semantic_search`. As model, we use [distilbert-multilingual-nli-stsb-quora-ranking](https://huggingface.co/sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking), which was trained to identify similar questions and supports 50+ languages. Hence, the user can input the question in any of the 50+ languages. This is a **symmetric search task**, as the search queries have the same length and content as the questions in the corpus.
 
 ### Similar Publication Retrieval
-[semantic_search_publications.py](semantic_search_publications.py) [ [Colab version](https://colab.research.google.com/drive/12hfBveGHRsxhPIUMmJYrll2lFU4fOX06?usp=sharing) ] shows an example how to find similar scientific publications. As corpus, we use all publications that have been presented at the EMNLP 2016 - 2018 conferences. As search query, we input the title and abstract of more recent publications and find related publications from our copurs. We use the [SPECTER](https://huggingface.co/sentence-transformers/allenai-specter) model. This is a **symmetric search task**, as the paper in the corpus consists of title & abstract and we search for title & abstract.
+[semantic_search_publications.py](semantic_search_publications.py) [ [Colab version](https://colab.research.google.com/drive/12hfBveGHRsxhPIUMmJYrll2lFU4fOX06?usp=sharing) ] shows an example how to find similar scientific publications. As corpus, we use all publications that have been presented at the EMNLP 2016 - 2018 conferences. As search query, we input the title and abstract of more recent publications and find related publications from our corpus. We use the [SPECTER](https://huggingface.co/sentence-transformers/allenai-specter) model. This is a **symmetric search task**, as the paper in the corpus consists of title & abstract and we search for title & abstract.
 
 ### Question & Answer Retrieval
 [semantic_search_wikipedia_qa.py](semantic_search_wikipedia_qa.py) [ [Colab Version](https://colab.research.google.com/drive/11GunvCqJuebfeTlgbJWkIMT0xJH6PWF1?usp=sharing) ]: This example uses a model that was trained on the [Natural Questions dataset](https://huggingface.co/datasets/sentence-transformers/natural-questions). It consists of about 100k real Google search queries, together with an annotated passage from Wikipedia that provides the answer. It is an example of an **asymmetric search task**. As corpus, we use the smaller [Simple English Wikipedia](https://simple.wikipedia.org/wiki/Main_Page) so that it fits easily into memory.