## Predicate: *correlate*

#### Roleset id: correlate.01 , *to be mutually related*, Source: , vncls: , framnet:

**correlate.01**: CORRELATE-V NOTES: Frame created by Claire (from correlate.01-v) CORRELATION-N NOTES: correlate.01 (from correlation.01-n)

#### Aliases:

Alias | FrameNet | VerbNet |

**correlate** (v.) | | |

**correlation** (n.) | | |

**correlated** (j.) | | |

#### Roles:

**Arg0-PAG**: *causer of relation* (vnrole: 22.2-2-1-Agent, 86.1-Agent)

**Arg1-PPT**: *topic, first entity in relation* (vnrole: 22.2-2-1-Patient, 86.1-Theme)

**Arg2-PPT**: *with what? second entity in relation* (vnrole: 22.2-2-1-co-patient, 86.1-co-theme)

#### Example: correlate-v: Linguist Favorite: sign and signified

In this restricted sense, signs-1 are causally correlated *-1 with thatwhich is signified.

**Argm-mnr**: In this restricted sense

**Argm-mnr**: causally

**Rel**: correlated

**Arg1**: *-1

**Arg2**: with that which is signified

#### Example: correlation-n: All args, inanimate causer

person: *ns*, tense: *ns*, aspect: *ns*, voice: *ns*, form: *ns*

The study's correlation between cell phone usage and cancer rates is tenuous at best.

**Arg0**: The study's

**Rel**: correlation

**Arg1**: between cell phone usage

**Arg2**: and cancer rates

## Predicate: *autocorrelate*

#### Roleset id: autocorrelate.02 , *cross-correlation of something (a signal?) with itself*, Source: , vncls: , framnet:

**autocorrelate.02**: AUTOCORRELATE-V NOTES: Added by Julia

#### Aliases:

Alias | FrameNet | VerbNet |

**autocorrelate** (v.) | | |

**auto-correlate** (v.) | | |

**auto_correlate** (v.) | | |

**autocorrelation** (n.) | | |

**auto-correlation** (n.) | | |

**auto_correlation** (v.) | | |

#### Roles:

**Arg0-PAG**: *agent/causer of correlation*

**Arg1-PPT**: *first (or all) instances correlated*

**Arg2-PPT**: *second instance correlated*

#### Example: autocorrelate-v

[If a time series of length N]-1 is autocorrelated *-1, the number of independent observations is fewer than N .

**Rel**: autocorrelated

**Arg1**: *-1

#### Example: autocorrelation-n

In statistics, the autocorrelation of a random process describes the correlation between values of the process at different times, as a function of the two times or of the time lag.

**Rel**: autocorrelation

**Arg1**: of a random process

## Predicate: *crosscorrelate*

#### Roleset id: crosscorrelate.03 , *compare one set of data against another*, Source: , vncls: , framnet:

**crosscorrelate.03**: CROSSCORRELATE-V NOTES: Added by Julia

#### Aliases:

Alias | FrameNet | VerbNet |

**crosscorrelate** (v.) | | |

**cross-correlate** (v.) | | |

**cross_correlate** (v.) | | |

**crosscorrelation** (n.) | | |

**cross-correlation** (n.) | | |

**cross_correlation** (v.) | | |

#### Roles:

**Arg0-PAG**: *agent/causer of correlation*

**Arg1-PPT**: *first (or all) set correlated*

**Arg2-PPT**: *second set*

#### Example: crosscorrelate-v

For each CMBR map, we generated 5000 realizations and cross-correlated them against each of the four galaxy maps.

**Argm-adv**: For each CMBR map

**Arg0**: we

**Rel**: cross-correlated

**Arg1**: them

**Arg2**: against each of our four galaxy maps

#### Example: cross_correlation-m

One approach to identifying a pattern within an image uses cross correlation of the image with a suitable mask.

**Rel**: [cross][correlation]

**Arg1**: of the image

**Arg2**: with a suitable mask