diff --git a/nlp/abbreviation_spellchecker_english.ipynb b/nlp/abbreviation_spellchecker_english.ipynb
index 715800f..cfdb2d4 100644
--- a/nlp/abbreviation_spellchecker_english.ipynb
+++ b/nlp/abbreviation_spellchecker_english.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Recently I bumped into a [question](https://stackoverflow.com/questions/43510778) on Stackoverflow, how to recover phrases from abbreviations, e.g. turn *wtrbtl* into *water bottle*, and *bsktball* into *basketball*. The question had an additional complication: lack of comprehensive list of word. That means, we need an algorithm able to invent new likely words.\n",
     "\n",
-    "I was intrigued, and started researching, which algorithms and math lie behind modern spell-checkers. It turned out that a good spell-checker can be made with a n-gram language model, a model of word distortions, and a greedy beam search algorithm. The whole construction is called a "noisy channel model" (see the "Spelling Correction and the Noisy Channel" section in  http://web.stanford.edu/~jurafsky/slp3 for more details). \n",
+    "I was intrigued, and started researching, which algorithms and math lie behind modern spell-checkers. It turned out that a good spell-checker can be made with a n-gram language model, a model of word distortions, and a greedy beam search algorithm. The whole construction is called a 'noisy channel model' (see the 'Spelling Correction and the Noisy Channel' section in http://web.stanford.edu/~jurafsky/slp3 for more details). \n",
     "\n",
     "With this knowledge and Python, I wrote a model from scratch. After training on \"The Fellowship of the Ring\" text, it was able to recognize abbreviations of modern sports terms."
    ]