Text Mining via information extraction
Identifieur interne :
000193 ( PascalFrancis/Corpus );
précédent :
000192;
suivant :
000194
Text Mining via information extraction
Auteurs : R. Feldman ;
Y. Aumann ;
M. Fresko ;
O. Liphstat ;
B. Rosenfeld ;
Y. SchlerSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 1999.
RBID : Pascal:99-0549347
Descripteurs français
English descriptors
Abstract
Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form, Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document. At one extreme, these labels are that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest. This paper presents an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on a more focused collection of events and phrases that are extracted from and label each document. These events plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process. This approach was implemented in the Textoscope system. Textoscope cpnsists of a document retrieval module which converts retrieved documents from their native formats into SGML documents used by Textoscope; an information extraction engine, which is based on a powerful attribute grammar which is augmented by a rich background knowledge; a taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and a set of knowledge-discovery tools for the resulting event-labeled documents. We evaluate our approach on a collection of newswire stories extracted by Textoscope's own agent. Our results confirm that Text Mining via information extraction serves as an accurate and powerful technique by which to manage knowledge encapsulated in large document collections.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 0302-9743 |
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| A05 | | | | @2 1704 |
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| A08 | 01 | 1 | ENG | @1 Text Mining via information extraction |
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| A09 | 01 | 1 | ENG | @1 PKDD'99 : principles of data mining and knowledge discovery : Prague, 15-18 September 1999 |
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| A11 | 01 | 1 | | @1 FELDMAN (R.) |
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| A11 | 02 | 1 | | @1 AUMANN (Y.) |
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| A11 | 03 | 1 | | @1 FRESKO (M.) |
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| A11 | 04 | 1 | | @1 LIPHSTAT (O.) |
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| A11 | 05 | 1 | | @1 ROSENFELD (B.) |
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| A11 | 06 | 1 | | @1 SCHLER (Y.) |
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| A12 | 01 | 1 | | @1 ZYTKOW (Jan M.) @9 ed. |
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| A12 | 02 | 1 | | @1 RAUCH (Jan) @9 ed. |
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| A14 | 01 | | | @1 Department of Mathematics and Computer Science, Bar-Ilan University @2 Ramat-Gan @3 ISR @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 4 aut. @Z 5 aut. @Z 6 aut. |
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| C01 | 01 | | ENG | @0 Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form, Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document. At one extreme, these labels are that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest. This paper presents an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on a more focused collection of events and phrases that are extracted from and label each document. These events plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process. This approach was implemented in the Textoscope system. Textoscope cpnsists of a document retrieval module which converts retrieved documents from their native formats into SGML documents used by Textoscope; an information extraction engine, which is based on a powerful attribute grammar which is augmented by a rich background knowledge; a taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and a set of knowledge-discovery tools for the resulting event-labeled documents. We evaluate our approach on a collection of newswire stories extracted by Textoscope's own agent. Our results confirm that Text Mining via information extraction serves as an accurate and powerful technique by which to manage knowledge encapsulated in large document collections. |
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| pR |
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Format Inist (serveur)
| NO : | PASCAL 99-0549347 INIST |
|---|
| ET : | Text Mining via information extraction |
|---|
| AU : | FELDMAN (R.); AUMANN (Y.); FRESKO (M.); LIPHSTAT (O.); ROSENFELD (B.); SCHLER (Y.); ZYTKOW (Jan M.); RAUCH (Jan) |
|---|
| AF : | Department of Mathematics and Computer Science, Bar-Ilan University/Ramat-Gan/Israël (1 aut., 2 aut., 3 aut., 4 aut., 5 aut., 6 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 1999; Vol. 1704; Pp. 165-173; Bibl. 7 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form, Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document. At one extreme, these labels are that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest. This paper presents an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on a more focused collection of events and phrases that are extracted from and label each document. These events plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process. This approach was implemented in the Textoscope system. Textoscope cpnsists of a document retrieval module which converts retrieved documents from their native formats into SGML documents used by Textoscope; an information extraction engine, which is based on a powerful attribute grammar which is augmented by a rich background knowledge; a taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and a set of knowledge-discovery tools for the resulting event-labeled documents. We evaluate our approach on a collection of newswire stories extracted by Textoscope's own agent. Our results confirm that Text Mining via information extraction serves as an accurate and powerful technique by which to manage knowledge encapsulated in large document collections. |
|---|
| CC : | 001D02B07D |
|---|
| FD : | Système intelligent; Système information; Traitement information; Traitement document; Extraction information; Règle association |
|---|
| ED : | Intelligent system; Information system; Information processing; Document processing; Information extraction |
|---|
| SD : | Sistema inteligente; Sistema información; Procesamiento información; Tratamiento documento; Extractión información |
|---|
| LO : | INIST-16343.354000084589530180 |
|---|
| ID : | 99-0549347 |
|---|
Links to Exploration step
Pascal:99-0549347
Le document en format XML
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</seriesStmt><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Document processing</term>
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</keywords><front><div type="abstract" xml:lang="en">Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form, Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document. At one extreme, these labels are that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest. This paper presents an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on a more focused collection of events and phrases that are extracted from and label each document. These events plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process. This approach was implemented in the Textoscope system. Textoscope cpnsists of a document retrieval module which converts retrieved documents from their native formats into SGML documents used by Textoscope; an information extraction engine, which is based on a powerful attribute grammar which is augmented by a rich background knowledge; a taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and a set of knowledge-discovery tools for the resulting event-labeled documents. We evaluate our approach on a collection of newswire stories extracted by Textoscope's own agent. Our results confirm that Text Mining via information extraction serves as an accurate and powerful technique by which to manage knowledge encapsulated in large document collections.</div>
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</fN21><pR><fA30 i1="01" i2="1" l="ENG"><s1>Principles of data mining and knowledge discovery. European conference</s1>
<s2>3</s2>
<s3>Prague CZE</s3>
<s4>1999-09-15</s4>
</fA30><server><NO>PASCAL 99-0549347 INIST</NO>
<ET>Text Mining via information extraction</ET>
<AU>FELDMAN (R.); AUMANN (Y.); FRESKO (M.); LIPHSTAT (O.); ROSENFELD (B.); SCHLER (Y.); ZYTKOW (Jan M.); RAUCH (Jan)</AU>
<AF>Department of Mathematics and Computer Science, Bar-Ilan University/Ramat-Gan/Israël (1 aut., 2 aut., 3 aut., 4 aut., 5 aut., 6 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 1999; Vol. 1704; Pp. 165-173; Bibl. 7 ref.</SO>
<LA>Anglais</LA>
<EA>Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form, Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document. At one extreme, these labels are that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest. This paper presents an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on a more focused collection of events and phrases that are extracted from and label each document. These events plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process. This approach was implemented in the Textoscope system. Textoscope cpnsists of a document retrieval module which converts retrieved documents from their native formats into SGML documents used by Textoscope; an information extraction engine, which is based on a powerful attribute grammar which is augmented by a rich background knowledge; a taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and a set of knowledge-discovery tools for the resulting event-labeled documents. We evaluate our approach on a collection of newswire stories extracted by Textoscope's own agent. Our results confirm that Text Mining via information extraction serves as an accurate and powerful technique by which to manage knowledge encapsulated in large document collections.</EA>
<CC>001D02B07D</CC>
<FD>Système intelligent; Système information; Traitement information; Traitement document; Extraction information; Règle association</FD>
<ED>Intelligent system; Information system; Information processing; Document processing; Information extraction</ED>
<SD>Sistema inteligente; Sistema información; Procesamiento información; Tratamiento documento; Extractión información</SD>
<LO>INIST-16343.354000084589530180</LO>
<ID>99-0549347</ID>
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