MorphAdorner >
Server >
Lemmatizer Service
|
NU
IT
Northwestern University Information Technology |
| MorphAdorner V2.0 | Site Map |
| Service name: | partofspeechtagger |
| Service description: | Adorn words with their parts of speech. |
| HTTP methods allowed: | GET, POST, OPTIONS |
| POST accepts as input: | application/x-www-form-urlencoded |
| HTTP return codes: | 200: service succeeded 400: service failed with an error |
Query parameters |
|
| corpusConfig | Corpus configuration name. In the standard distribution these are ece, eme, and ncf. |
| media | Result format. One of json, xml, html, text . |
| text | Text to be processed. |
| includeInputText | Allowed values are true to include the input text in the output and false to not include the input text. |
| outputReg | Output standardized spelling in TEI XML format. Allowed values are true to output the standard spelling, false to not output the standard spelling. |
| XML output style. | Select outputPlainXML for plain XML, outputTEI for TEI format XML, or outputTCF for WebLicht TCF format XML. |
<form accept-charset="UTF-8" method="post" action="partofspeechtagger"
target="_blank"
name="postagger">
<table cellpadding="0" cellspacing="5">
<tr>
<td><strong>Text:</strong></td>
<td colspan="2">
<textarea name="text" rows="15" cols="76"></textarea>
</td>
</tr>
<tr>
<td valign="top">
<strong>
Lexicon:</strong>
</td>
<td>
<input type="radio" name="corpusConfig" value="eme">Early Modern English</input><br />
<input type="radio" name="corpusConfig" value="ece">Eighteen Century English</input><br />
<input type="radio" name="corpusConfig" value="ncf" checked="checked">Nineteenth Century Fiction</input>
</td>
</tr>
<tr>
<td> </td>
<td>
<input type="checkbox" name="includeInputText" value="true"
checked="checked"/>
Include input text in results
</td>
</tr>
<tr>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td valign="top">
<strong>Results format:</strong>
</td>
<td>
<input type="radio" name="media" value="json">JSON format</input><br />
<input type="radio" name="media" value="xml" checked="checked">XML format</input><br />
<input type="radio" name="xmlOutputType" value="outputPlainXML" checked="checked">Plain XML</input><br />
<input type="radio" name="xmlOutputType" value="outputTEI">Fragmentary TEI format XML</input><br />
<input type="checkbox" name="outputReg" value="false" />Add reg= attribute for standard spelling<br />
<input type="radio" name="xmlOutputType" value="outputTCF">WebLicht TCF format XML</input><br />
<input type="radio" name="media" value="html">HTML format</input><br />
<input type="radio" name="media" value="text">Text format</input>
</td>
</tr>
<tr>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td colspan="2">
<input type="submit" name="adorn" value="Adorn" />
</td>
</tr>
</table>
</form>
Here we adorn the first two sentences of Sarah Hale's poem "Mary had a little lamb."
Mary had a little lamb,
whose fleece was white as snow.
And everywhere that Mary went,
the lamb was sure to go.
The JSON and XML PartOfSpeechTaggerResult echoes the input text and the corpusConfig. The sentences container wraps a sequence of sentence entries each of which represents a single parsed sentence from the input text. Each sentence contains a sequence of token entries representing the words and punctuation in the sentence. Following this is an adornedSentences container which contains a sequence of adornedSentence entries. Each adornedSentence contains a sequence of adornedWord entries containing the morphological adornments.
For XML format output, the alternate output formats provide different formatting but the same basic information.
The HTML and text versions provide tabular versions of the adorned sentences.
{
"PartOfSpeechTaggerResult": {
"text": "Mary had a little lamb, whose fleece was white as snow. And everywhere that Mary went, the lamb was sure to go.",
"corpusConfig": "ncf",
"sentences": [
{
"sentence": [
{
"token": [
"Mary",
"had",
"a",
"little",
"lamb",
",",
"whose",
"fleece",
"was",
"white",
"as",
"snow",
"."
]
},
{
"token": [
"And",
"everywhere",
"that",
"Mary",
"went",
",",
"the",
"lamb",
"was",
"sure",
"to",
"go",
"."
]
}
]
}
],
"adornedSentences": [
{
"adornedSentence": [
{
"adornedWord": [
{
"token": "Mary",
"spelling": "Mary",
"standardSpelling": "Mary",
"lemmata": "Mary",
"partsOfSpeech": "np1"
},
{
"token": "had",
"spelling": "had",
"standardSpelling": "had",
"lemmata": "have",
"partsOfSpeech": "vhd"
},
{
"token": "a",
"spelling": "a",
"standardSpelling": "a",
"lemmata": "a",
"partsOfSpeech": "dt"
},
{
"token": "little",
"spelling": "little",
"standardSpelling": "little",
"lemmata": "little",
"partsOfSpeech": "j"
},
{
"token": "lamb",
"spelling": "lamb",
"standardSpelling": "lamb",
"lemmata": "lamb",
"partsOfSpeech": "n1"
},
{
"token": ",",
"spelling": ",",
"standardSpelling": ",",
"lemmata": ",",
"partsOfSpeech": ","
},
{
"token": "whose",
"spelling": "whose",
"standardSpelling": "whose",
"lemmata": "who",
"partsOfSpeech": "r-crq"
},
{
"token": "fleece",
"spelling": "fleece",
"standardSpelling": "fleece",
"lemmata": "fleece",
"partsOfSpeech": "n1"
},
{
"token": "was",
"spelling": "was",
"standardSpelling": "was",
"lemmata": "be",
"partsOfSpeech": "vbds"
},
{
"token": "white",
"spelling": "white",
"standardSpelling": "white",
"lemmata": "white",
"partsOfSpeech": "j-jn"
},
{
"token": "as",
"spelling": "as",
"standardSpelling": "as",
"lemmata": "as",
"partsOfSpeech": "c-acp"
},
{
"token": "snow",
"spelling": "snow",
"standardSpelling": "snow",
"lemmata": "snow",
"partsOfSpeech": "n1"
},
{
"token": ".",
"spelling": ".",
"standardSpelling": ".",
"lemmata": ".",
"partsOfSpeech": "."
}
]
},
{
"adornedWord": [
{
"token": "And",
"spelling": "And",
"standardSpelling": "And",
"lemmata": "and",
"partsOfSpeech": "cc"
},
{
"token": "everywhere",
"spelling": "everywhere",
"standardSpelling": "everywhere",
"lemmata": "everywhere",
"partsOfSpeech": "av"
},
{
"token": "that",
"spelling": "that",
"standardSpelling": "that",
"lemmata": "that",
"partsOfSpeech": "cst"
},
{
"token": "Mary",
"spelling": "Mary",
"standardSpelling": "Mary",
"lemmata": "Mary",
"partsOfSpeech": "np1"
},
{
"token": "went",
"spelling": "went",
"standardSpelling": "went",
"lemmata": "go",
"partsOfSpeech": "vvd"
},
{
"token": ",",
"spelling": ",",
"standardSpelling": ",",
"lemmata": ",",
"partsOfSpeech": ","
},
{
"token": "the",
"spelling": "the",
"standardSpelling": "the",
"lemmata": "the",
"partsOfSpeech": "dt"
},
{
"token": "lamb",
"spelling": "lamb",
"standardSpelling": "lamb",
"lemmata": "lamb",
"partsOfSpeech": "n1"
},
{
"token": "was",
"spelling": "was",
"standardSpelling": "was",
"lemmata": "be",
"partsOfSpeech": "vbds"
},
{
"token": "sure",
"spelling": "sure",
"standardSpelling": "sure",
"lemmata": "sure",
"partsOfSpeech": "j"
},
{
"token": "to",
"spelling": "to",
"standardSpelling": "to",
"lemmata": "to",
"partsOfSpeech": "pc-acp"
},
{
"token": "go",
"spelling": "go",
"standardSpelling": "go",
"lemmata": "go",
"partsOfSpeech": "vvi"
},
{
"token": ".",
"spelling": ".",
"standardSpelling": ".",
"lemmata": ".",
"partsOfSpeech": "."
}
]
}
]
}
],
"outputTEI": false,
"outputReg": false,
"outputTCF": false
}
}
<?xml version="1.0"?>
<PartOfSpeechTaggerResult>
<text>Mary had a little lamb, whose fleece was white as snow. And everywhere that Mary went, the lamb was sure to go.</text>
<corpusConfig>ncf</corpusConfig>
<sentences>
<sentence>
<token>Mary</token>
<token>had</token>
<token>a</token>
<token>little</token>
<token>lamb</token>
<token>,</token>
<token>whose</token>
<token>fleece</token>
<token>was</token>
<token>white</token>
<token>as</token>
<token>snow</token>
<token>.</token>
</sentence>
<sentence>
<token>And</token>
<token>everywhere</token>
<token>that</token>
<token>Mary</token>
<token>went</token>
<token>,</token>
<token>the</token>
<token>lamb</token>
<token>was</token>
<token>sure</token>
<token>to</token>
<token>go</token>
<token>.</token>
</sentence>
</sentences>
<adornedSentences>
<adornedSentence>
<adornedWord>
<token>Mary</token>
<spelling>Mary</spelling>
<standardSpelling>Mary</standardSpelling>
<lemmata>Mary</lemmata>
<partsOfSpeech>np1</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>had</token>
<spelling>had</spelling>
<standardSpelling>had</standardSpelling>
<lemmata>have</lemmata>
<partsOfSpeech>vhd</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>a</token>
<spelling>a</spelling>
<standardSpelling>a</standardSpelling>
<lemmata>a</lemmata>
<partsOfSpeech>dt</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>little</token>
<spelling>little</spelling>
<standardSpelling>little</standardSpelling>
<lemmata>little</lemmata>
<partsOfSpeech>j</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>lamb</token>
<spelling>lamb</spelling>
<standardSpelling>lamb</standardSpelling>
<lemmata>lamb</lemmata>
<partsOfSpeech>n1</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>,</token>
<spelling>,</spelling>
<standardSpelling>,</standardSpelling>
<lemmata>,</lemmata>
<partsOfSpeech>,</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>whose</token>
<spelling>whose</spelling>
<standardSpelling>whose</standardSpelling>
<lemmata>who</lemmata>
<partsOfSpeech>r-crq</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>fleece</token>
<spelling>fleece</spelling>
<standardSpelling>fleece</standardSpelling>
<lemmata>fleece</lemmata>
<partsOfSpeech>n1</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>was</token>
<spelling>was</spelling>
<standardSpelling>was</standardSpelling>
<lemmata>be</lemmata>
<partsOfSpeech>vbds</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>white</token>
<spelling>white</spelling>
<standardSpelling>white</standardSpelling>
<lemmata>white</lemmata>
<partsOfSpeech>j-jn</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>as</token>
<spelling>as</spelling>
<standardSpelling>as</standardSpelling>
<lemmata>as</lemmata>
<partsOfSpeech>c-acp</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>snow</token>
<spelling>snow</spelling>
<standardSpelling>snow</standardSpelling>
<lemmata>snow</lemmata>
<partsOfSpeech>n1</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>.</token>
<spelling>.</spelling>
<standardSpelling>.</standardSpelling>
<lemmata>.</lemmata>
<partsOfSpeech>.</partsOfSpeech>
</adornedWord>
</adornedSentence>
<adornedSentence>
<adornedWord>
<token>And</token>
<spelling>And</spelling>
<standardSpelling>And</standardSpelling>
<lemmata>and</lemmata>
<partsOfSpeech>cc</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>everywhere</token>
<spelling>everywhere</spelling>
<standardSpelling>everywhere</standardSpelling>
<lemmata>everywhere</lemmata>
<partsOfSpeech>av</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>that</token>
<spelling>that</spelling>
<standardSpelling>that</standardSpelling>
<lemmata>that</lemmata>
<partsOfSpeech>cst</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>Mary</token>
<spelling>Mary</spelling>
<standardSpelling>Mary</standardSpelling>
<lemmata>Mary</lemmata>
<partsOfSpeech>np1</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>went</token>
<spelling>went</spelling>
<standardSpelling>went</standardSpelling>
<lemmata>go</lemmata>
<partsOfSpeech>vvd</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>,</token>
<spelling>,</spelling>
<standardSpelling>,</standardSpelling>
<lemmata>,</lemmata>
<partsOfSpeech>,</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>the</token>
<spelling>the</spelling>
<standardSpelling>the</standardSpelling>
<lemmata>the</lemmata>
<partsOfSpeech>dt</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>lamb</token>
<spelling>lamb</spelling>
<standardSpelling>lamb</standardSpelling>
<lemmata>lamb</lemmata>
<partsOfSpeech>n1</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>was</token>
<spelling>was</spelling>
<standardSpelling>was</standardSpelling>
<lemmata>be</lemmata>
<partsOfSpeech>vbds</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>sure</token>
<spelling>sure</spelling>
<standardSpelling>sure</standardSpelling>
<lemmata>sure</lemmata>
<partsOfSpeech>j</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>to</token>
<spelling>to</spelling>
<standardSpelling>to</standardSpelling>
<lemmata>to</lemmata>
<partsOfSpeech>pc-acp</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>go</token>
<spelling>go</spelling>
<standardSpelling>go</standardSpelling>
<lemmata>go</lemmata>
<partsOfSpeech>vvi</partsOfSpeech>
</adornedWord>
<adornedWord>
<token>.</token>
<spelling>.</spelling>
<standardSpelling>.</standardSpelling>
<lemmata>.</lemmata>
<partsOfSpeech>.</partsOfSpeech>
</adornedWord>
</adornedSentence>
</adornedSentences>
<outputTEI>false</outputTEI>
<outputReg>false</outputReg>
<outputTCF>false</outputTCF>
</PartOfSpeechTaggerResult>
<h3>26 words in 2 sentences found. </h3> <table border="0"> <tbody><tr> <th align="left">S#</th><th align="left">W#</th><th align="left">Spelling</th><th align="left">Pos</th><th align="left">Standard</th><th align="left">Lemma</th></tr> <tr><td>1</td><td>1</td><td>Mary</td><td>np1</td><td>Mary</td><td>Mary</td></tr> <tr><td>1</td><td>2</td><td>had</td><td>vhd</td><td>had</td><td>have</td></tr> <tr><td>1</td><td>3</td><td>a</td><td>dt</td><td>a</td><td>a</td></tr> <tr><td>1</td><td>4</td><td>little</td><td>j</td><td>little</td><td>little</td></tr> <tr><td>1</td><td>5</td><td>lamb</td><td>n1</td><td>lamb</td><td>lamb</td></tr> <tr><td>1</td><td>6</td><td>,</td><td>,</td><td>,</td><td>,</td></tr> <tr><td>1</td><td>7</td><td>whose</td><td>r-crq</td><td>whose</td><td>who</td></tr> <tr><td>1</td><td>8</td><td>fleece</td><td>n1</td><td>fleece</td><td>fleece</td></tr> <tr><td>1</td><td>9</td><td>was</td><td>vbds</td><td>was</td><td>be</td></tr> <tr><td>1</td><td>10</td><td>white</td><td>j-jn</td><td>white</td><td>white</td></tr> <tr><td>1</td><td>11</td><td>as</td><td>c-acp</td><td>as</td><td>as</td></tr> <tr><td>1</td><td>12</td><td>snow</td><td>n1</td><td>snow</td><td>snow</td></tr> <tr><td>1</td><td>13</td><td>.</td><td>.</td><td>.</td><td>.</td></tr> <tr><td>2</td><td>1</td><td>And</td><td>cc</td><td>And</td><td>and</td></tr> <tr><td>2</td><td>2</td><td>everywhere</td><td>av</td><td>everywhere</td><td>everywhere</td></tr> <tr><td>2</td><td>3</td><td>that</td><td>cst</td><td>that</td><td>that</td></tr> <tr><td>2</td><td>4</td><td>Mary</td><td>np1</td><td>Mary</td><td>Mary</td></tr> <tr><td>2</td><td>5</td><td>went</td><td>vvd</td><td>went</td><td>go</td></tr> <tr><td>2</td><td>6</td><td>,</td><td>,</td><td>,</td><td>,</td></tr> <tr><td>2</td><td>7</td><td>the</td><td>dt</td><td>the</td><td>the</td></tr> <tr><td>2</td><td>8</td><td>lamb</td><td>n1</td><td>lamb</td><td>lamb</td></tr> <tr><td>2</td><td>9</td><td>was</td><td>vbds</td><td>was</td><td>be</td></tr> <tr><td>2</td><td>10</td><td>sure</td><td>j</td><td>sure</td><td>sure</td></tr> <tr><td>2</td><td>11</td><td>to</td><td>pc-acp</td><td>to</td><td>to</td></tr> <tr><td>2</td><td>12</td><td>go</td><td>vvi</td><td>go</td><td>go</td></tr> <tr><td>2</td><td>13</td><td>.</td><td>.</td><td>.</td><td>.</td></tr> </tbody> </table>
| S# | W# | Spelling | Pos | Standard | Lemma |
|---|---|---|---|---|---|
| 1 | 1 | Mary | np1 | Mary | Mary |
| 1 | 2 | had | vhd | had | have |
| 1 | 3 | a | dt | a | a |
| 1 | 4 | little | j | little | little |
| 1 | 5 | lamb | n1 | lamb | lamb |
| 1 | 6 | , | , | , | , |
| 1 | 7 | whose | r-crq | whose | who |
| 1 | 8 | fleece | n1 | fleece | fleece |
| 1 | 9 | was | vbds | was | be |
| 1 | 10 | white | j-jn | white | white |
| 1 | 11 | as | c-acp | as | as |
| 1 | 12 | snow | n1 | snow | snow |
| 1 | 13 | . | . | . | . |
| 2 | 1 | And | cc | And | and |
| 2 | 2 | everywhere | av | everywhere | everywhere |
| 2 | 3 | that | cst | that | that |
| 2 | 4 | Mary | np1 | Mary | Mary |
| 2 | 5 | went | vvd | went | go |
| 2 | 6 | , | , | , | , |
| 2 | 7 | the | dt | the | the |
| 2 | 8 | lamb | n1 | lamb | lamb |
| 2 | 9 | was | vbds | was | be |
| 2 | 10 | sure | j | sure | sure |
| 2 | 11 | to | pc-acp | to | to |
| 2 | 12 | go | vvi | go | go |
| 2 | 13 | . | . | . | . |
26 words in 2 sentences found. S# W# Spelling Pos Standard Lemma 1 1 Mary np1 Mary Mary 1 2 had vhd had have 1 3 a dt a a 1 4 little j little little 1 5 lamb n1 lamb lamb 1 6 , , , , 1 7 whose r-crq whose who 1 8 fleece n1 fleece fleece 1 9 was vbds was be 1 10 white j-jn white white 1 11 as c-acp as as 1 12 snow n1 snow snow 1 13 . . . . 2 1 And cc And and 2 2 everywhere av everywhere everywhere 2 3 that cst that that 2 4 Mary np1 Mary Mary 2 5 went vvd went go 2 6 , , , , 2 7 the dt the the 2 8 lamb n1 lamb lamb 2 9 was vbds was be 2 10 sure j sure sure 2 11 to pc-acp to to 2 12 go vvi go go 2 13 . . . .
 |
Home |
| Welcome | |
|
Announcements and News |
| Announcements and news about changes to MorphAdorner | |
|
Documentation |
| Documentation for using MorphAdorner | |
|
Download MorphAdorner |
| Downloading and installing the MorphAdorner client and server software | |
|
Glossary |
| Glossary of MorphAdorner terms | |
|
Helpful References |
| Natural language processing references | |
|
Licenses |
| Licenses for MorphAdorner and Associated Software | |
|
Server |
| Online examples of MorphAdorner Server facilities. | |
|
Talks |
| Slides from talks about MorphAdorner. | |
|
Tech Talk |
| Technical information for programmers using MorphAdorner |
|
|
Academic Technologies and Research Services,
NU Library 2East, 1970 Campus Drive Evanston, IL 60208. |
Contact Us.
|
