CA2481080A1 - Method and system for detecting and extracting named entities from spontaneous communications - Google Patents
Method and system for detecting and extracting named entities from spontaneous communications Download PDFInfo
- Publication number
- CA2481080A1 CA2481080A1 CA002481080A CA2481080A CA2481080A1 CA 2481080 A1 CA2481080 A1 CA 2481080A1 CA 002481080 A CA002481080 A CA 002481080A CA 2481080 A CA2481080 A CA 2481080A CA 2481080 A1 CA2481080 A1 CA 2481080A1
- Authority
- CA
- Canada
- Prior art keywords
- contextual
- named
- named entities
- detected
- communications
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1815—Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L2015/088—Word spotting
Abstract
The invention concerns a method and system for detecting and extracting name d entities from spontaneous communications (Fig.1). The method may recognizing input communications from a user (150), detecting contextual named entities (160) from the recognized input communications (150) and outputting the contextual named entities to a language understanding unit (170).
Claims (19)
1. A method for processing input communications with a user, comprising:
recognizing input communications from the user;
detecting contextual named entities from the recognized input communications; and outputting the contextual named entities to a language understanding unit.
recognizing input communications from the user;
detecting contextual named entities from the recognized input communications; and outputting the contextual named entities to a language understanding unit.
2. The method of claim 1, further comprising:
producing a lattice from the recognized communications, wherein the contextual named entities are detected from the lattice.
producing a lattice from the recognized communications, wherein the contextual named entities are detected from the lattice.
3. The method of claim 1, wherein the contextual named entities are detected using a named entity language model.
4. The method of claim 1, further comprising:
inserting named entity tags into the detected contextual named entities.
inserting named entity tags into the detected contextual named entities.
5. The method of claim 1, further comprising:
classifying the input communications according to confidence scores.
classifying the input communications according to confidence scores.
6. The method of claim 1, further comprising:
performing a composition function using a named entity language model.
performing a composition function using a named entity language model.
7. The method of claim 6, wherein the composition function is a matching technique.
8. The method of claim 1, further comprising:
determining N-best values for each named entity detected.
determining N-best values for each named entity detected.
9. The method of claim 1, wherein outputting step outputs N-best named entity values to the language understanding unit.
10. The method of claim 1, wherein the input communications include communications in one or more languages.
11. The method of claim 1, wherein the input communications include at least one of verbal and non-verbal speech.
12. The method of claim 11, wherein the non-verbal speech includes the use of at least one of gestures, body movements, head movements, non-responses, text, keyboard entries, keypad entries, mouse clicks, DTMF codes, pointers, stylus, cable set-top box entries, graphical user interface entries and touchscreen entries.
13. The method of claim 1, wherein the input communications include multimodal speech.
14. The method of claim 1, further comprising:
making processing decisions based on the detected contextual named entities.
making processing decisions based on the detected contextual named entities.
15. The method of claim 1, wherein the named entities are represented by at least one of a tag, a context and a value.
16. A system that processes input communication with a user, comprising:
a recognizer that recognizes input communications from the user;
and a named entity detector that detects contextual named entities from the recognized input communication, and outputs the contextual named entities to a language understanding unit.
a recognizer that recognizes input communications from the user;
and a named entity detector that detects contextual named entities from the recognized input communication, and outputs the contextual named entities to a language understanding unit.
17. The system of claim 16, wherein the recognizer produces a lattice from the recognized communications, and the named entity detector detects the contextual named entities from the lattice.
18. The system of claim 16, wherein the named entity detector detects the contextual named entities using a named entity language model.
19. The system of claim 16, further comprising:
a named entity tagger that inserts named entity tags into the detected contextual named entities.
a task classification processor makes task classification decisions based on the detected contextual named entities.
32. The task classification system of claim 31, wherein the task classification processor includes a dialogue manager that conducts dialogue with the user based on the detected named entities.
33. The task classification system of claim 31, wherein the task classification processor includes a language understanding unit that computes a confidence function to determine whether the user's input communication can be classified according to task.
34. The task classification system of claim 33, wherein if the task cannot be classified, the dialogue manager conducts dialogue with the user based on the detected contextual named entities.
a named entity tagger that inserts named entity tags into the detected contextual named entities.
a task classification processor makes task classification decisions based on the detected contextual named entities.
32. The task classification system of claim 31, wherein the task classification processor includes a dialogue manager that conducts dialogue with the user based on the detected named entities.
33. The task classification system of claim 31, wherein the task classification processor includes a language understanding unit that computes a confidence function to determine whether the user's input communication can be classified according to task.
34. The task classification system of claim 33, wherein if the task cannot be classified, the dialogue manager conducts dialogue with the user based on the detected contextual named entities.
Applications Claiming Priority (7)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US30762402P | 2002-04-05 | 2002-04-05 | |
| US60/307,624 | 2002-04-05 | ||
| US44364203P | 2003-01-29 | 2003-01-29 | |
| US60/443,642 | 2003-01-29 | ||
| US40294103A | 2003-04-01 | 2003-04-01 | |
| US10/402,941 | 2003-04-01 | ||
| PCT/US2003/010482 WO2003088080A1 (en) | 2002-04-05 | 2003-04-07 | Method and system for detecting and extracting named entities from spontaneous communications |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2481080A1 true CA2481080A1 (en) | 2003-10-23 |
| CA2481080C CA2481080C (en) | 2010-10-12 |
Family
ID=29255289
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2481080A Expired - Fee Related CA2481080C (en) | 2002-04-05 | 2003-04-07 | Method and system for detecting and extracting named entities from spontaneous communications |
Country Status (4)
| Country | Link |
|---|---|
| EP (1) | EP1497751A4 (en) |
| AU (1) | AU2003224846A1 (en) |
| CA (1) | CA2481080C (en) |
| WO (1) | WO2003088080A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10629205B2 (en) | 2018-06-12 | 2020-04-21 | International Business Machines Corporation | Identifying an accurate transcription from probabilistic inputs |
Families Citing this family (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8478589B2 (en) | 2005-01-05 | 2013-07-02 | At&T Intellectual Property Ii, L.P. | Library of existing spoken dialog data for use in generating new natural language spoken dialog systems |
| US8185399B2 (en) | 2005-01-05 | 2012-05-22 | At&T Intellectual Property Ii, L.P. | System and method of providing an automated data-collection in spoken dialog systems |
| US9892208B2 (en) | 2014-04-02 | 2018-02-13 | Microsoft Technology Licensing, Llc | Entity and attribute resolution in conversational applications |
| US9798708B1 (en) | 2014-07-11 | 2017-10-24 | Google Inc. | Annotating relevant content in a screen capture image |
| US9965559B2 (en) | 2014-08-21 | 2018-05-08 | Google Llc | Providing automatic actions for mobile onscreen content |
| US9703541B2 (en) | 2015-04-28 | 2017-07-11 | Google Inc. | Entity action suggestion on a mobile device |
| US10229674B2 (en) | 2015-05-15 | 2019-03-12 | Microsoft Technology Licensing, Llc | Cross-language speech recognition and translation |
| CN105070289B (en) * | 2015-07-06 | 2017-11-17 | 百度在线网络技术（北京）有限公司 | English name-to recognition methods and device |
| US10803391B2 (en) * | 2015-07-29 | 2020-10-13 | Google Llc | Modeling personal entities on a mobile device using embeddings |
| US10970646B2 (en) | 2015-10-01 | 2021-04-06 | Google Llc | Action suggestions for user-selected content |
| US10178527B2 (en) | 2015-10-22 | 2019-01-08 | Google Llc | Personalized entity repository |
| US10055390B2 (en) | 2015-11-18 | 2018-08-21 | Google Llc | Simulated hyperlinks on a mobile device based on user intent and a centered selection of text |
| US10535005B1 (en) | 2016-10-26 | 2020-01-14 | Google Llc | Providing contextual actions for mobile onscreen content |
| US11237696B2 (en) | 2016-12-19 | 2022-02-01 | Google Llc | Smart assist for repeated actions |
| US10311874B2 (en) | 2017-09-01 | 2019-06-04 | 4Q Catalyst, LLC | Methods and systems for voice-based programming of a voice-controlled device |
| CN109785840B (en) * | 2019-03-05 | 2021-01-29 | 湖北亿咖通科技有限公司 | Method and device for identifying natural language, vehicle-mounted multimedia host and computer readable storage medium |
| US11790172B2 (en) | 2020-09-18 | 2023-10-17 | Microsoft Technology Licensing, Llc | Systems and methods for identifying entities and constraints in natural language input |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5212730A (en) * | 1991-07-01 | 1993-05-18 | Texas Instruments Incorporated | Voice recognition of proper names using text-derived recognition models |
| US6173261B1 (en) * | 1998-09-30 | 2001-01-09 | At&T Corp | Grammar fragment acquisition using syntactic and semantic clustering |
| US5832480A (en) * | 1996-07-12 | 1998-11-03 | International Business Machines Corporation | Using canonical forms to develop a dictionary of names in a text |
| US6044337A (en) * | 1997-10-29 | 2000-03-28 | At&T Corp | Selection of superwords based on criteria relevant to both speech recognition and understanding |
| WO2000062193A1 (en) * | 1999-04-08 | 2000-10-19 | Kent Ridge Digital Labs | System for chinese tokenization and named entity recognition |
-
2003
- 2003-04-07 EP EP03721540A patent/EP1497751A4/en not_active Withdrawn
- 2003-04-07 WO PCT/US2003/010482 patent/WO2003088080A1/en not_active Application Discontinuation
- 2003-04-07 CA CA2481080A patent/CA2481080C/en not_active Expired - Fee Related
- 2003-04-07 AU AU2003224846A patent/AU2003224846A1/en not_active Abandoned
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10629205B2 (en) | 2018-06-12 | 2020-04-21 | International Business Machines Corporation | Identifying an accurate transcription from probabilistic inputs |
Also Published As
| Publication number | Publication date |
|---|---|
| AU2003224846A1 (en) | 2003-10-27 |
| EP1497751A4 (en) | 2009-10-21 |
| WO2003088080A1 (en) | 2003-10-23 |
| CA2481080C (en) | 2010-10-12 |
| EP1497751A1 (en) | 2005-01-19 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request | ||
| MKLA | Lapsed |
Effective date: 20210407 |
|
| MKLA | Lapsed |
Effective date: 20210407 |