Systems and methods include obtaining a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflects activity within the information technology or security environment. Thereafter, a first neural network is used to automatically identify variable text to extract as a field from the set of events. An indication of the variable text is provided as a field extraction recommendation, for example, to a user device for presentation to a user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: obtaining a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflecting activity within the information technology or security environment; using a first neural network to automatically identify variable text to extract as a field value from the set of events; and providing an indication of the variable text as a field extraction recommendation. 2. The computer-implemented method of claim 1, wherein the set of events correspond with a source type and the first neural network is generated in accordance with a set of training events corresponding with the source type. 3. The computer-implemented method of claim 1 further comprising training the first neural network. 4. The computer-implemented method of claim 1 further comprising training the first neural network, wherein the first neural network is trained by: obtaining a set of training events comprising character strings; providing the character strings to the first neural network on a character-by-character basis; for each provided character, predicting a next character; and training the first neural network based on a comparison of the predicted next character to an actual next character. 5. The computer-implemented method of claim 1, wherein the first neural network is used to automatically identify variable text to extract by: providing the set of events as character strings to the first neural network on a character-by-character basis; for each character provided to the first neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a deviation in the probabilities; and utilizing the deviation in the probabilities to identify the variable text to extract. 6. The computer-implemented method of claim 1, wherein the first neural network is used to automatically identify variable text to extract by: providing the set of events as character strings to the first neural network on a character-by-character basis; for each character provided to the first neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a first probability that falls below a threshold value; and designating the character associated with the first probability as a beginning character of the variable text. 7. The computer-implemented method of claim 1 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events. 8. The computer-implemented method of claim 1 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, wherein the first neural network is trained in a forward character-by-character basis, and the second neural network is trained in a reverse character-by-character basis. 9. The computer-implemented method of claim 1 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, wherein character strings are provided the first neural network in a forward manner beginning with the first characters of the character strings, and the character strings are provided to the second neural network in a reverse manner beginning with the last characters of the character strings. 10. The computer-implemented method of claim 1 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, the second neural network being trained by: obtaining a set of training events comprising character strings; providing the character strings to the second neural network on a character-by-character basis beginning at last characters of the character strings; for each provided character, predicting a next character; and training the second neural network based on a comparison of the predicted next character to an actual next character. 11. The computer-implemented method of claim 1 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, the second neural network being trained by: providing the set of events as character strings to the second neural network on a character-by-character basis beginning at last characters of the character strings; for each character provided to the second neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a deviation in the probabilities; and utilizing the deviation in the probabilities to identify the variable text to extract. 12. The computer-implemented method of claim 1, further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, the second neural network being trained by: providing the set of events as character strings to the second neural network on a character-by-character basis beginning at last characters of the character strings; for each character provided to the second neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a first probability that falls below a threshold value; and designating the character associated with the first probability as an ending character of the variable text. 13. The computer-implemented method of claim 1 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, wherein the first neural network is used to identify a beginning of a variable text within a character string, and the second neural network is used to identifying an ending of the variable text within the character string. 14. The computer-implemented method of claim 1 further comprising causing display of the indication of the variable text as the field extraction recommendation. 15. The computer-implemented method of claim 1, wherein the indication of the variable text comprises the identified variable text. 16. The computer-implemented method of claim 1, wherein the indication of the variable text comprises a representation of the identified variable text. 17. The computer-implemented method of claim 1 further comprising generating an extraction rule to extract the variable text. 18. The computer-implemented method of claim 1 further comprising: causing display of the indication of the variable text as the field extraction recommendation; receiving a selection of the field extraction recommendation; and providing a preview of field values to be extracted in accordance with the field extraction recommendation. 19. The computer-implemented method of claim 1 further comprising: causing display of the indication of the variable text as the field extraction recommendation; receiving a selection of the field extraction recommendation; and based on the selection, initiating a corresponding field extraction. 20. The computer-implemented method of claim 1 further comprising: causing display of the indication of the variable text as the field extraction recommendation; receiving a selection of the field extraction recommendation; and generating an extraction rule to extract the variable text. 21. A system comprising: one or more processors; and computer memory having instructions stored thereon, the instructions, when executed by the one or more processors causing the system to perform a method comprising: obtain a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflecting activity within the information technology or security environment; use a first neural network to automatically identify variable text to extract as a field value from the set of events; and provide an indication of the variable text as a field extraction recommendation. 22. The system of claim 21 further comprising training the first neural network, wherein the first neural network is trained by: obtaining a set of training events comprising character strings; providing the character strings to the first neural network on a character-by-character basis; for each provided character, predicting a next character; and training the first neural network based on a comparison of the predicted next character to an actual next character. 23. The system of claim 21, wherein the first neural network is used to automatically identify variable text to extract by: providing the set of events as character strings to the first neural network on a character-by-character basis; for each character provided to the first neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a deviation in the probabilities; and utilizing the deviation in the probabilities to identify the variable text to extract. 24. The system of claim 21, wherein the first neural network is used to automatically identify variable text to extract by: providing the set of events as character strings to the first neural network on a character-by-character basis; for each character provided to the first neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a first probability that falls below a threshold value; and designating the character associated with the first probability as a beginning character of the variable text. 25. The system of claim 21 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, wherein the first neural network is trained in a forward character-by-character basis, and the second neural network is trained in a reverse character-by-character basis. 26. One or more computer-readable media having instructions stored thereon, the instructions, when executed by a processor of a computing device, to cause the computing device to perform a method comprising: obtain a set of events, each event in the set of events comprising a time-stamped portion of raw machine data, the raw machine data produced by one or more components within an information technology or security environment and reflecting activity within the information technology or security environment; use a first neural network to automatically identify variable text to extract as a field value from the set of events; and provide an indication of the variable text as a field extraction recommendation. 27. The one or more computer-readable media of claim 26 further comprising training the first neural network, wherein the first neural network is trained by: obtaining a set of training events comprising character strings; providing the character strings to the first neural network on a character-by-character basis; for each provided character, predicting a next character; and training the first neural network based on a comparison of the predicted next character to an actual next character. 28. The one or more computer-readable media of claim 26, wherein the first neural network is used to automatically identify variable text to extract by: providing the set of events as character strings to the first neural network on a character-by-character basis; for each character provided to the first neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a deviation in the probabilities; and utilizing the deviation in the probabilities to identify the variable text to extract. 29. The one or more computer-readable media of claim 26, wherein the first neural network is used to automatically identify variable text to extract by: providing the set of events as character strings to the first neural network on a character-by-character basis; for each character provided to the first neural network, identifying a probability of a next character given the previous characters; analyzing a sequence of the probabilities to identify a first probability that falls below a threshold value; and designating the character associated with the first probability as a beginning character of the variable text. 30. The one or more computer-readable media of claim 26 further comprising using a second neural network to automatically identify the variable text to extract as the field value from the set of events, wherein the first neural network is trained in a forward character-by-character basis, and the second neural network is trained in a reverse character-by-character basis.