Grammartize - De-Dupe, Extract Keywords & Tag the Parts-of-Speech in your Text

Your automated text processing friend

Grammartize will take a paragraph(s) and clean it up for you. Just copy-and-paste! It will also extract keywords from your text, and identify the parts of speech for each word in each sentence

Here's an example. This paragraph:

this is a poorly written sentence . because it has many incorrect casings and punctuation . and it will also remove <b>HTML tags</b> so you can even use this tool to scrape text from another website, clean it automatically and extract keywords! you can use the parts-of-speech it detects to help power a semantic natural language processing algorithm

Turns into:

Cleaned Up Text

This is a poorly written sentence. Because it has many incorrect casings and punctuation. And it will also remove HTML tags so you can even use this tool to scrape text from another website, clean it automatically and extract keywords! You can use the parts-of-speech it detects to help power a semantic natural language processing algorithm.

Extracted Keywords

sentence casings punctuation HTML tags tool text website keywords! parts-of-speech power language processing algorithm

JSON with grammar properties, including parts-of-speech

{
    "keywords": [
        "casings",
        "punctuation",
        "HTML",
        "tags",
        "tool",
        "text",
        "website",
        "keywords!",
        "parts-of-speech",
        "power",
        "language",
        "processing"
    ],
    "sentences": [
        {
            "sentence": "This is a poorly written sentence.",
            "parts_of_speech": [
                {
                    "word": "This",
                    "pos": "DT",
                    "probability": 0.9649410482478001
                },
                {
                    "word": "is",
                    "pos": "VBZ",
                    "probability": 0.9996415929373801
                },
                {
                    "word": "a",
                    "pos": "DT",
                    "probability": 0.9925710129429816
                },
                {
                    "word": "poorly",
                    "pos": "RB",
                    "probability": 0.9755468189257864
                },
                {
                    "word": "written",
                    "pos": "VBN",
                    "probability": 0.9887884599759417
                }
            ]
        },
        {
            "sentence": "Because it has many incorrect casings and punctuation.",
            "parts_of_speech": [
                {
                    "word": "Because",
                    "pos": "IN",
                    "probability": 0.9751000318954289
                },
                {
                    "word": "it",
                    "pos": "PRP",
                    "probability": 0.9844816089456473
                },
                {
                    "word": "has",
                    "pos": "VBZ",
                    "probability": 0.9941074010942099
                },
                {
                    "word": "many",
                    "pos": "JJ",
                    "probability": 0.9313453137084935
                },
                {
                    "word": "incorrect",
                    "pos": "JJ",
                    "probability": 0.8951782136494861
                },
                {
                    "word": "casings",
                    "pos": "NNS",
                    "probability": 0.9939335260290271
                },
                {
                    "word": "and",
                    "pos": "CC",
                    "probability": 0.8934999845483217
                },
                {
                    "word": "punctuation",
                    "pos": "NN",
                    "probability": 0.6157912447287361
                }
            ]
        },
        {
            "sentence": "And it will also remove HTML tags so you can even use this tool to scrape text from another website, clean it automatically and extract keywords!",
            "parts_of_speech": [
                {
                    "word": "And",
                    "pos": "CC",
                    "probability": 0.9864183899669506
                },
                {
                    "word": "it",
                    "pos": "PRP",
                    "probability": 0.9860953296211228
                },
                {
                    "word": "will",
                    "pos": "MD",
                    "probability": 0.9953053912147346
                },
                {
                    "word": "also",
                    "pos": "RB",
                    "probability": 0.9879004669062599
                },
                {
                    "word": "remove",
                    "pos": "VB",
                    "probability": 0.9837234961225233
                },
                {
                    "word": "HTML",
                    "pos": "NNP",
                    "probability": 0.5629321529291051
                },
                {
                    "word": "tags",
                    "pos": "NNS",
                    "probability": 0.8172902069899364
                },
                {
                    "word": "so",
                    "pos": "IN",
                    "probability": 0.658459012415309
                },
                {
                    "word": "you",
                    "pos": "PRP",
                    "probability": 0.9943618757910981
                },
                {
                    "word": "can",
                    "pos": "MD",
                    "probability": 0.9914345630142958
                },
                {
                    "word": "even",
                    "pos": "RB",
                    "probability": 0.9895991445811851
                },
                {
                    "word": "use",
                    "pos": "VB",
                    "probability": 0.9919004720541796
                },
                {
                    "word": "this",
                    "pos": "DT",
                    "probability": 0.9751947784139334
                },
                {
                    "word": "tool",
                    "pos": "NN",
                    "probability": 0.9636308481895044
                },
                {
                    "word": "to",
                    "pos": "TO",
                    "probability": 0.9455681935953136
                },
                {
                    "word": "scrape",
                    "pos": "VB",
                    "probability": 0.9239541111002356
                },
                {
                    "word": "text",
                    "pos": "NN",
                    "probability": 0.9297737777777552
                },
                {
                    "word": "from",
                    "pos": "IN",
                    "probability": 0.9807016027702872
                },
                {
                    "word": "another",
                    "pos": "DT",
                    "probability": 0.9773203524078873
                },
                {
                    "word": "website",
                    "pos": "NN",
                    "probability": 0.7115274715023365
                },
                {
                    "word": "clean",
                    "pos": "VB",
                    "probability": 0.5821669273530655
                },
                {
                    "word": "it",
                    "pos": "PRP",
                    "probability": 0.9407144791980238
                },
                {
                    "word": "automatically",
                    "pos": "RB",
                    "probability": 0.9688919392001865
                },
                {
                    "word": "and",
                    "pos": "CC",
                    "probability": 0.9235531141608554
                },
                {
                    "word": "extract",
                    "pos": "VB",
                    "probability": 0.8401214150658921
                },
                {
                    "word": "keywords!",
                    "pos": "NN",
                    "probability": 0.3443318624986281
                }
            ]
        },
        {
            "sentence": "You can use the parts-of-speech it detects to help power a semantic natural language processing algorithm.",
            "parts_of_speech": [
                {
                    "word": "You",
                    "pos": "PRP",
                    "probability": 0.9848020051528957
                },
                {
                    "word": "can",
                    "pos": "MD",
                    "probability": 0.9934236547107167
                },
                {
                    "word": "use",
                    "pos": "VB",
                    "probability": 0.9912312310101712
                },
                {
                    "word": "the",
                    "pos": "DT",
                    "probability": 0.9696350660989163
                },
                {
                    "word": "parts-of-speech",
                    "pos": "NN",
                    "probability": 0.9182308943441783
                },
                {
                    "word": "it",
                    "pos": "PRP",
                    "probability": 0.942223804992029
                },
                {
                    "word": "detects",
                    "pos": "VBZ",
                    "probability": 0.9264716959944886
                },
                {
                    "word": "to",
                    "pos": "TO",
                    "probability": 0.9862271039503585
                },
                {
                    "word": "help",
                    "pos": "VB",
                    "probability": 0.973796357878615
                },
                {
                    "word": "power",
                    "pos": "NN",
                    "probability": 0.48859974119805144
                },
                {
                    "word": "a",
                    "pos": "DT",
                    "probability": 0.90298163024588
                },
                {
                    "word": "semantic",
                    "pos": "JJ",
                    "probability": 0.9914245680269631
                },
                {
                    "word": "natural",
                    "pos": "JJ",
                    "probability": 0.9271380633820155
                },
                {
                    "word": "language",
                    "pos": "NN",
                    "probability": 0.9949373954267862
                },
                {
                    "word": "processing",
                    "pos": "NN",
                    "probability": 0.8301762854354489
                }
            ]
        }
    ]
}

The "pos" tags (VBZ, NN, etc) are defined by the Penn Treebank Parts-of-Speech Project.

Grammartize!

Clean Your Text!

Copy-and-paste your text to get it
cleaned, parsed, tokenized and organized!

Your automated text processing friend

Here's an example. This paragraph:

Turns into:

Cleaned Up Text

Extracted Keywords

JSON with grammar properties, including parts-of-speech

Tools

Other Usefull Tools

De-Dupe Text

Keyword Extraction

Parts-Of-speech

API