<p><strong>TL;DR</strong>: Simply pick an existing system which is seems easy to implement for you and seems to have reasonable accuracy. This can either be a cloud offering (for example, IBM Watson Conversation, Google DialogFlow) or an library or executable (for example, RASA NLU or Natural Language Toolkit). Choosing a system solely on accuracy is non-trivial and if you always want the best, then you should switch between systems often.</p>
<p>You question asks which system will give the most accurate results while not requiring too much computational power. In your case for recognizing a person name from a text. The natural language processing (NLP) field is rapidly changing. To show this, we can look at the current state of the art (SOTA) for named-entity recognition (NER). <a href="https://github.com/dsindex/etagger" rel="nofollow noreferrer">This</a> Github page has a nice summary for the CONLL03 NER dataset, I will copy it here and use company names since they are easier to remember:</p>
<ol>
<li><a href="http://alanakbik.github.io/papers/coling2018.pdf" rel="nofollow noreferrer">Zalando</a>. F1 score: 0.931. Date: 24 June 2018</li>
<li><a href="https://arxiv.org/pdf/1810.04805.pdf" rel="nofollow noreferrer">Google</a>. F1 score: 0.928. Date: 31 October 2018</li>
<li><a href="https://arxiv.org/pdf/1809.08370.pdf" rel="nofollow noreferrer">Stanford / Google Brain</a>. F1 score: 0.926. Date: 22 September 2018</li>
</ol>
<p>Based on this list we observe that, at the start of 2019, a new SOTA is obtained every few months. See <a href="https://rajpurkar.github.io/SQuAD-explorer/" rel="nofollow noreferrer">https://rajpurkar.github.io/SQuAD-explorer/</a> for an updated list of benchmarks for a complex NLP task. So, since the SOTA algorithm changes each month, &quot;the most accurate system (library)&quot; also has to change often. Furthermore, the accuracy on your data depends not only on the system, but also on the following:</p>
<ul>
<li><strong>Used algorithm.</strong> It could be that Google has published SOTA research but not implemented it. The only way to figure it out, for sure, is continually testing all systems.</li>
<li><strong>Training data size.</strong> Although bigger is better, some algorithms can handle few examples (<em>few-shot learning</em>) better.</li>
<li><strong>Domain.</strong> An algorithm could be better suitable for handling formal governmental text instead of less formal Wikipedia text.</li>
<li><strong>Data language.</strong> Since most research is focused on showing SOTA on public data sets, they are often optimized for English. How they perform on other languages might differ.</li>
</ul>
<p>Due to all these things to consider, I would advise to pick an existing system and choose based on many requirements such as pricing and ease of use.</p>
compare NER library from Stanford coreNLP, SpaCy And Google cloud
<p>I want to recognise <strong>person</strong> name from text. But i'm getting confused which NLP library I have to use for NER. I find out following best NLP library for NER
1. Stanford coreNLP
2. Spacy
3. Google cloud.</p>

<p>I unable to find out which library will give more accurate result and good performance. Please help me here.</p>