Separate sentences on each new line from paragraph using python
<p>I have paragraph as:</p>

<p><strong>INPUT :--</strong></p>

<p>"However, there is generally a lack of local, regional, and national land use and land cover data of sufficient reliability and temporal and geographic detail for providing accurate estimates of landscape change. The U.S. Geological Survey's EROS Data Center and the Landscape Ecology Branch of the U.S. Environmental Protection Agency are collaborating on a four-year research project to document the types, distributions, rates, drivers, and consequences of land cover change for the conterminous United States over the past 30 years. The project is using an ecoregion framework as a geographic stratifier."</p>

<p><em>Wants to separate every sentence on new line. I am parsing for "." replacing every "." as "\n" (new line character). It works fine for normal sentence, but when "The U.S. Geological..." such things came my script make 2 separate sentences which i don't want. Please suggest anything possible</em></p>

<p><strong>Expected Output:-- (3 sentences numbered serially )</strong></p>

<p>1) However, there is generally a lack of local, regional, and national land use and land cover data of sufficient reliability and temporal and geographic detail for providing accurate estimates of landscape change. </p>

<p>2) The U.S. Geological Survey's EROS Data Center and the Landscape Ecology Branch of the U.S. Environmental Protection Agency are collaborating on a four-year research project to document the types, distributions, rates, drivers, and consequences of land cover change for the conterminous United States over the past 30 years. </p>

<p>3) The project is using an ecoregion framework as a geographic stratifier.</p>

<p><strong>Currently Getting: (7 sentences)</strong></p>

<p>1) However, there is generally a lack of local, regional, and national land use and land cover data of sufficient reliability and temporal and geographic detail for providing accurate estimates of landscape change.</p>

<p>2) The U.</p>

<p>3) S. </p>

<p>4) Geological Survey's EROS Data Center and the Landscape Ecology Branch of the U.</p>

<p>5) S. </p>

<p>6) Environmental Protection Agency are collaborating on a four-year research project to document the types, distributions, rates, drivers, and consequences of land cover change for the conterminous United States over the past 30 years.</p>

<p>7) The project is using an ecoregion framework as a geographic stratifier.</p>

<p>Using <code>nltk</code> is definately a good approach. The sentances could be enumerated as follows:</p>

<pre><code>import nltk

text = "However, there is generally a lack of local, regional, and national land use and land cover data of sufficient reliability and temporal and geographic detail for providing accurate estimates of landscape change. The U.S. Geological Survey's EROS Data Center and the Landscape Ecology Branch of the U.S. Environmental Protection Agency are collaborating on a four-year research project to document the types, distributions, rates, drivers, and consequences of land cover change for the conterminous United States over the past 30 years. The project is using an ecoregion framework as a geographic stratifier."                
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')

for index, sentence in enumerate(tokenizer.tokenize(text), start=1):
    print "{}) {}\n".format(index, sentence)
</code></pre>

<p>This would display the following output:</p>

<pre class="lang-none prettyprint-override"><code>1) However, there is generally a lack of local, regional, and national land use and land cover data of sufficient reliability and temporal and geographic detail for providing accurate estimates of landscape change.

2) The U.S. Geological Survey's EROS Data Center and the Landscape Ecology Branch of the U.S. Environmental Protection Agency are collaborating on a four-year research project to document the types, distributions, rates, drivers, and consequences of land cover change for the conterminous United States over the past 30 years.

3) The project is using an ecoregion framework as a geographic stratifier.
</code></pre>

python parsing