EP1131812A2 - Procede et dispositif servant a ameliorer l'identification de parties vocales - Google Patents

Procede et dispositif servant a ameliorer l'identification de parties vocales

Info

Publication number
EP1131812A2
EP1131812A2 EP99972347A EP99972347A EP1131812A2 EP 1131812 A2 EP1131812 A2 EP 1131812A2 EP 99972347 A EP99972347 A EP 99972347A EP 99972347 A EP99972347 A EP 99972347A EP 1131812 A2 EP1131812 A2 EP 1131812A2
Authority
EP
European Patent Office
Prior art keywords
speech
taggers
specialized
text
output
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.)
Withdrawn
Application number
EP99972347A
Other languages
German (de)
English (en)
Inventor
Alwin B. Carus
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Lernout and Hauspie Speech Products NV
Original Assignee
Lernout and Hauspie Speech Products NV
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Lernout and Hauspie Speech Products NV filed Critical Lernout and Hauspie Speech Products NV
Publication of EP1131812A2 publication Critical patent/EP1131812A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars

Definitions

  • the present invention relates generally to part-of-speech tagging of text and more particularly to the contextual part-of-speech disambiguation of words and phrases in text.
  • a tagging device for identifying parts-of-speech of terms in a text comprises a first part-of-speech tagger, a set of specialized part-of-speech-taggers and an exception handler.
  • the word “set” refers to a set that includes at least one member.
  • the first part-of-speech tagger provides, at a first output, a part-of-speech tag for each term in the text.
  • the word "term” refers to a word and optionally to a word or a phrase.
  • the tagging device is operative on each word in the text, and optionally the tagger may be operative as well on phrases in the text.
  • the set of specialized part-of-speech taggers has an output coupled to a device output and also has an input and provides a set of candidate part-of-speech tags for each term provided at the input to the set of specialized part-of-speech taggers.
  • the exception handler coupled to the first output, provides, in response to each term in the text, a part-of-speech tag from the first output to the device output, unless the term in the text is included in an exception list. If the term in the text is included in the exception list, the term is provided to the input of the set of specialized part-of-speech taggers.
  • the set of specialized part of speech taggers includes a plurality of specialized part-of-speech taggers and the tagging device further includes a selector, coupled to the output of the set of specialized part-of- speech taggers.
  • the selector also has an output coupled to the device output. The selector selects a part-of-speech tag from the set of candidate part-of-speech tags using a voting procedure and provides the selected part-of-speech tag at the device output.
  • at least one member of the set of specialized part-of-speech taggers is optimized for processing terms on the exception list.
  • the exception list may include terms which account for a predetermined percentage of errors produced by the first part-of-speech tagger.
  • the voting procedure generates a score for each unique candidate part-of-speech tag from the set of candidate part-of- speech tags based on predetermined characteristics of each specialized part-of- speech tagger in the set of specialized part-of-speech taggers. The voting procedure may select the part-of-speech tag with the highest score.
  • the tagging device may further include a tokenizer, coupled to the first part-of-speech tagger, for parsing the text into a set of word tokens.
  • a method for identifying parts-of-speech of terms in a text comprises: (a) using a first part-of-speech tagger to determine the part-of-speech of each term in the text; (b) identifying each term in the text which is included in an exception list; (c) providing the part-of-speech tag from step (a) as a device output for each term not included in the exception list; and (d) using a set of specialized part-of-speech taggers to determine a set of candidate part-of- speech tags for each term included in the exception list.
  • the method wherein the set of specialized part-of-speech taggers includes a plurality of taggers, further includes (e) selecting a part-of-speech tag from the set of candidate part-of-speech tags using a voting procedure and (f) providing the part of speech tag selected in step (e) as the device output for each term included in the exception list
  • At least one member of the set of specialized part-of-speech taggers is optimized for processing terms on the exception list
  • the exception list may include terms which account for a predetermined percentage of errors produced by step (a)
  • the vohng procedure generates a score for each unique candidate part-of-speech tag from the set of candidate part-of-speech tags, the score being based upon predetermined characteristics of each specialized part-of-speech tagger in the set of specialized part-of-speech taggers
  • the voting procedure may select the candidate part-of-speech tag with the highest score
  • the method may further include, before step (a), parsing the text into word tokens
  • a digital storage medium encoded with instructions which, when loaded into a computer, may establish any of the devices previously discussed
  • Fig 1 is a block diagram of a tagging device in accordance with an embodiment of the invention
  • Fig 2 is a block diagram showing the voting procedure utilized by the tagging device of Fig 1 in accordance with a preferred embodiment of the invention
  • FIG 3 is a block diagram showing the flow of control for a method of part- of-speech tagging in accordance with an embodiment of the invention
  • Figure 1 shows a block diagram of a tagging device in accordance with an embodiment of the invention.
  • Text is input at a text input 10 and then the text is parsed into word tokens using a tokenizer 11.
  • the tokenizer 11 may be one of general use in the art (for example, U.S. Patent No. 5,721,939, "Method and Apparatus for Toker ⁇ zing Text", or U.S. Patent No. 4,991,094, "Method for Language-Independent Text Tokenization using a Character Categorization", herein incorporated by reference).
  • the tokenized text is then placed into a text buffer in order to be processed by the tagging device.
  • a first part of speech tagger 12 processes the tokenized text.
  • the first part-of-speech tagger 12 may be one of general use in the art such as Markov model, decision tree, connectionist, transformational, nearest neighbor, on-line learning, or maximum entropy.
  • the first part-of-speech tagger 12 is a fast and accurate part-of-speech tagger.
  • the first part-of-speech tagger 12 is a Brill transformational tagger implemented by an Abney-like finite-state automaton (FSA) (See Brill, E., "Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging," Computational Linguistics 21 (4), Dec. 1995, pp. 543-565, herein incorporated by reference). Accordingly, the first part of speech tagger 12 is produced by generating Brill part-of-speech tagging transformational rules against a first part- of-speech tagged corpus of text.
  • FSA Abney-like finite-state automaton
  • An exception handler 13 is coupled to the first part-of-speech tagger 12. If the term being processed is not found on an exception list, the part-of-speech tag identified by the first part of speech tagger will be the output 19 of the tagging device. When the exception handler 13 encounters a term found on the exception list, the term is routed to a set of specialized part-of- speech taggers 14- 17 coupled to the exception handler 13 for further processing.
  • the set of specialized part-of-speech taggers may include n members, where n can be a number greater than or equal to one. In the embodiment shown in Figure 1, the set of specialized part-of-speech taggers includes four specialized part-of-speech taggers 14-17.
  • the exception list includes terms which are known to have inaccurate tagging results using the first part-of-speech tagger 12.
  • the terms included in the exception list are identified by running the first part-of-speech tagger 12 against a second part-of-speech tagged corpus of text to identify the residual error of the first part-of-speech tagger 12.
  • the frequency distribution of the part-of-speech tagging errors by the term associated with the errors is generated in order to identify the terms which account for the most frequently occurring errors produced by the first part-of-speech tagger 12.
  • Terms which account for a predetermined percentage of the errors produced by the first part- of-speech tagger 12 are included in the exception list. In one embodiment, the predetermined percentage is 90%.
  • Each specialized part-of-speech tagger is generated using the exception list described above.
  • Each of the specialized part-of-speech taggers 14-17 may be one generally known in the art. As discussed above, some examples of part-of - speech taggers are the Markov model, decision tree, connectionist, transformational, nearest neighbor, on-line learning, and maximum entropy.
  • the specialized part-of-speech taggers are produced by the methods appropriate to each style of tagger, however, each specialized part-of-speech tagger is trained specifically on the terms included in the exception list. Preferably, each specialized part-of-speech tagger is of a different type.
  • the specialized part-of-speech taggers 14-17 are trigram, Brill transformational, memory-based learning, and maximum entropy part-of-speech taggers. If there is only one specialized part-of-speech tagger in the set of specialized part-of-speech taggers, the output of the specialized part-of-speech tagger is the device output 19 for each term in the text that is included in the exception list. As discussed above, for each term not found on the exception list, the device output 19 will be the output of the first part-of-speech tagger 12.
  • each specialized part-of-speech tagger 14-17 will produce a candidate part-of-speech tag for the term being processed by the set of specialized part-of-speech taggers.
  • Each candidate part- of-speech tag produced by the set of specialized part of speech taggers is provided to a selector 18.
  • the selector 18 uses a voting procedure to select one of the candidate part-of-speech tags.
  • Figure 2 is a block diagram showing the voting procedure according to an embodiment of the invention.
  • each specialized part-of-speech tagger processes the term and identifies a candidate part-of-speech tag.
  • the voting procedure creates a list of unique candidate part-of-speech tags identified by the specialized part-of-speech taggers.
  • a score is then calculated for each unique candidate part-of-speech tag at block 22.
  • the voting procedure uses pre-computed values of precision and recall for each specialized part-of-speech tagger to calculate a score (block 22) for each unique candidate part-of-speech tag produced by the set of specialized part-of-speech taggers.
  • Precision is defined as the percentage of tokens tagged X by the part-of-speech tagger that are also tagged X in the training corpus. Recall is defined as the percentage of tokens tagged X in a training corpus that are also tagged X by the part-of-speech tagger.
  • the values of precision and recall may be used to determine the score for each unique candidate part-of-speech tag at block 22.
  • the score for a candidate part-of-speech tag may be calculated by adding the precision of each specialized part-of-speech tagger which produced a particular candidate part-of-speech tag to an amount equal to (1-recall) of each specialized part-of-speech tagger which produced the particular candidate part-of-speech tag.
  • the candidate part-of-speech tag with the highest accumulated score is selected at block 23 as the part-of-speech tag for the term being processed by the set of specialized part-of-speech taggers.
  • Table 1 shows example results for the word "that” using a set of specialized part-of-speech taggers consisting of trigram, Brill transformational, memory-based learning, and maximum entropy part-of-speech taggers.
  • the candidate part-of-speech tags are defined as determiner (DT) and coordinating conjunction(CS).
  • the candidate part-of-speech tag CS has the higher score and would be selected as the part-of-speech tag for the word "that".
  • the output 19 of the tagging device will be the output of selector 18 for each term in the text that is found on the exception list. Otherwise, the output 19 of the tagging device will be the output of the first part- of-speech tagger 12.
  • the use of the specialized part-of-speech taggers 14-17 in combination with the first part-of-speech tagger 12 improves the performance and accuracy of the first part-of-speech tagger 12. This is accomplished by training each specialized part of speech tagger 14-17 to improve the accuracy of those terms which produce the largest error rates for the first part-of-speech tagger 12.
  • Figure 3 illustrates the flow of control for a method of identifying the parts-of-speech of terms in a text in accordance with an embodiment of the invention.
  • the text input at block 30 is parsed into word tokens at block 31.
  • the tokenized text is then placed in a text buffer at block 32 and processed at block 33 by a first part-of-speech tagger.
  • the output at block 37 will be the part-of-speech tag produced by the first part-of-speech tagger.
  • the exception list is described above with respect to Figure 1. If the term being processed is found on the exception list, the term will be processed by a set specialized part-of-speech taggers at block 35.
  • the set of specialized part-of-speech taggers produce a set of candidate part-of-speech tags. If the set of specialized part of speech taggers includes only one specialized part-of-speech tagger, the output at block 37 will be the output of the specialized part-of-speech tagger as determined at block 35. If the set of specialized part-of-speech taggers includes a plurality of specialized part-of-speech taggers, a voting procedure is used at block 36 to select a part-of- speech tag from the set of candidate part-of-speech tags. The voting procedure for an embodiment of the invention is described above with respect to Figure 2. At block 37 the output for a term in the text that is found on the exception list will be the part-of-speech tag selected in step 36.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Machine Translation (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Time Recorders, Dirve Recorders, Access Control (AREA)

Abstract

Dispositif servant à identifier des parties vocales de texte et comprenant un premier identificateur assignant, à une première sortie, une étiquette de partie vocale pour chaque terme du texte et un ensemble d'identificateurs spécialisés dont la sortie est couplée à une sortie du dispositif et possédant également une entrée. L'ensemble d'identificateurs spécialisés assigne un ensemble d'étiquettes de parties vocales candidates pour chaque terme présenté à l'entrée de l'ensemble des identificateurs spécialisés. Un pilote d'exception couplé à la première sortie assigne, en réponse à chaque terme du texte, une étiquette de partie vocale depuis la première sortie jusqu'à la sortie du dispositif, à moins que le terme du texte soit inclus dans une liste d'exception, dans ce cas, ce terme étant présent à l'entrée de l'ensemble d'identificateurs spécialisés de parties vocales. On peut mettre en oeuvre un processus de vote afin de sélectionner une étiquette de partie vocale dans l'ensemble d'étiquettes candidates produites par les identificateurs spécialisés pour des termes figurant sur la liste d'exception.
EP99972347A 1998-11-17 1999-11-17 Procede et dispositif servant a ameliorer l'identification de parties vocales Withdrawn EP1131812A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US10877898P 1998-11-17 1998-11-17
US108778P 1998-11-17
PCT/US1999/027210 WO2000030070A2 (fr) 1998-11-17 1999-11-17 Procede et dispositif servant a ameliorer l'identification de parties vocales

Publications (1)

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EP1131812A2 true EP1131812A2 (fr) 2001-09-12

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EP (1) EP1131812A2 (fr)
JP (1) JP2002530761A (fr)
AU (1) AU3789900A (fr)
CA (1) CA2351404A1 (fr)
WO (1) WO2000030070A2 (fr)

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