comp479's Introduction
Reuters-21578 text categorization test collection Distribution 1.0 README file (v 1.2) 26 September 1997 David D. Lewis AT&T Labs - Research [email protected] I. Introduction This README describes Distribution 1.0 of the Reuters-21578 text categorization test collection, a resource for research in information retrieval, machine learning, and other corpus-based research. II. Copyright & Notification The copyright for the text of newswire articles and Reuters annotations in the Reuters-21578 collection resides with Reuters Ltd. Reuters Ltd. and Carnegie Group, Inc. have agreed to allow the free distribution of this data *for research purposes only*. If you publish results based on this data set, please acknowledge its use, refer to the data set by the name "Reuters-21578, Distribution 1.0", and inform your readers of the current location of the data set (see "Availability & Questions"). III. Availability & Questions The Reuters-21578, Distribution 1.0 test collection is available from David D. Lewis' professional home page, currently: http://www.research.att.com/~lewis Besides this README file, the collection consists of 22 data files, an SGML DTD file describing the data file format, and six files describing the categories used to index the data. (See Sections VI and VII for more details.) Some additional files, which are not part of the collection but have been contributed by other researchers as useful resources are also included. All files are available uncompressed, and in addition a single gzipped Unix tar archive of the entire distribution is available as reuters21578.tar.gz. The text categorization mailing list, DDLBETA, is a good place to send questions about this collection and other text categorization issues. You may join the list by writing David Lewis at [email protected]. IV. History & Acknowledgements The documents in the Reuters-21578 collection appeared on the Reuters newswire in 1987. The documents were assembled and indexed with categories by personnel from Reuters Ltd. (Sam Dobbins, Mike Topliss, Steve Weinstein) and Carnegie Group, Inc. (Peggy Andersen, Monica Cellio, Phil Hayes, Laura Knecht, Irene Nirenburg) in 1987. In 1990, the documents were made available by Reuters and CGI for research purposes to the Information Retrieval Laboratory (W. Bruce Croft, Director) of the Computer and Information Science Department at the University of Massachusetts at Amherst. Formatting of the documents and production of associated data files was done in 1990 by David D. Lewis and Stephen Harding at the Information Retrieval Laboratory. Further formatting and data file production was done in 1991 and 1992 by David D. Lewis and Peter Shoemaker at the Center for Information and Language Studies, University of Chicago. This version of the data was made available for anonymous FTP as "Reuters-22173, Distribution 1.0" in January 1993. From 1993 through 1996, Distribution 1.0 was hosted at a succession of FTP sites maintained by the Center for Intelligent Information Retrieval (W. Bruce Croft, Director) of the Computer Science Department at the University of Massachusetts at Amherst. At the ACM SIGIR '96 conference in August, 1996 a group of text categorization researchers discussed how published results on Reuters-22173 could be made more comparable across studies. It was decided that a new version of collection should be produced with less ambiguous formatting, and including documentation carefully spelling out standard methods of using the collection. The opportunity would also be used to correct a variety of typographical and other errors in the categorization and formatting of the collection. Steve Finch and David D. Lewis did this cleanup of the collection September through November of 1996, relying heavily on Finch's SGML-tagged version of the collection from an earlier study. One result of the re-examination of the collection was the removal of 595 documents which were exact duplicates (based on identity of timestamps down to the second) of other documents in the collection. The new collection therefore has only 21,578 documents, and thus is called the Reuters-21578 collection. This README describes version 1.0 of this new collection, which we refer to as "Reuters-21578, Distribution 1.0". In preparing the collection and documentation we have benefited from discussions with Eric Brown, William Cohen, Fred Damerau, Yoram Singer, Amit Singhal, and Yiming Yang, among many others. We thank all the people and organizations listed above for their efforts and support, without which this collection would not exist. A variety of other changes were also made in going from Reuters-22173 to Reuters-21578: 1. Documents were marked up with SGML tags, and a corresponding SGML DTD was produced, so that the boundaries of important sections of documents (e.g. category fields) are unambiguous. 2. The set of categories that are legal for each of the five controlled vocabulary fields was specified. All category names not legal for a field were corrected to a legal category, moved to their appropriate field, or removed, as appropriate. 3. Documents were given new ID numbers, in chronological order, and are collected 1000 to a file in order by ID (and therefore in order chronologically). V. What is a Text Categorization Test Collection and Who Cares? *Text categorization* is the task of deciding whether a piece of text belongs to any of a set of prespecified categories. It is a generic text processing task useful in indexing documents for later retrieval, as a stage in natural language processing systems, for content analysis, and in many other roles [LEWIS94d]. The use of standard, widely distributed test collections has been a considerable aid in the development of algorithms for the related task of *text retrieval* (finding documents that satisfy a particular user's information need, usually expressed in an textual request). Text retrieval test collections have allowed the comparison of algorithms developed by a variety of researchers around the world. (For more on text retrieval test collections see SPARCKJONES76.) Standard test collections have been lacking, however, for text categorization. Few data sets have been used by more than one researcher, making results hard to compare. The Reuters-22173 test collection has been used in a number of published studies since it was made available, and we believe that the Reuters-21578 collection will be even more valuable. The collection may also be of interest to researchers in machine learning, as it provides a classification task with challenging properties. There are multiple categories, the categories are overlapping and nonexhaustive, and there are relationships among the categories. There are interesting possibilities for the use of domain knowledge. There are many possible feature sets that can be extracted from the text, and most plausible feature/example matrices are large and sparse. There is even some temporal structure to the data [LEWIS94b], though problems with the indexing and the uneven distribution of stories within the timespan covered may make this collection a poor one to explore temporal issues. VI. Formatting The Reuters-21578 collection is distributed in 22 files. Each of the first 21 files (reut2-000.sgm through reut2-020.sgm) contain 1000 documents, while the last (reut2-021.sgm) contains 578 documents. The files are in SGML format. Rather than going into the details of the SGML language, we describe here in an informal way how the SGML tags are used to divide each file, and each document, into sections. Readers interested in more detail on SGML are encouraged to pursue one of the many books and web pages on the subject. Each of the 22 files begins with a document type declaration line: <!DOCTYPE lewis SYSTEM "lewis.dtd"> The DTD file lewis.dtd is included in the distribution. Following the document type declaration line are individual Reuters articles marked up with SGML tags, as described below. VI.A. The REUTERS tag: Each article starts with an "open tag" of the form <REUTERS TOPICS=?? LEWISSPLIT=?? CGISPLIT=?? OLDID=?? NEWID=??> where the ?? are filled in an appropriate fashion. Each article ends with a "close tag" of the form: </REUTERS> In all cases the <REUTERS> and </REUTERS> tags are the only items on their line. Each REUTERS tag contains explicit specifications of the values of five attributes, TOPICS, LEWISSPLIT, CGISPLIT, OLDID, and NEWID. These attributes are meant to identify documents and groups of documents, and have the following meanings: 1. TOPICS : The possible values are YES, NO, and BYPASS: a. YES indicates that *in the original data* there was at least one entry in the TOPICS fields. b. NO indicates that *in the original data* the story had no entries in the TOPICS field. c. BYPASS indicates that *in the original data* the story was marked with the string "bypass" (or a typographical variant on that string). This poorly-named attribute unfortunately is the subject of much confusion. It is meant to indicate whether or not the document had TOPICS categories *in the raw Reuters-22173 dataset*. The sole use of this attribute is to defining training set splits similar to those used in previous research. (See the section on training set splits.) The TOPICS attribute does **NOT** indicate anything about whether or not the Reuters-21578 document has any TOPICS categories. (Version 1.0 of this document was errorful on this point.) That can be determined by actually looking at the TOPICS field. A story with TOPICS="YES" can have no TOPICS categories, and a story with TOPICS="NO" can have TOPICS categories. Now, a reasonable (though not certain) assumption is that for all TOPICS="YES" stories the indexer at least thought about whether the story belonged to a valid TOPICS category. Thus, the TOPICS="YES" stories with no topics can reasonably be considered negative examples for all 135 valid TOPICS categories. TOPICS="NO" stories are more problematic in their interpretation. Some of them presumedly result because the indexer made an explicit decision that they did not belong to any of the 135 valid TOPICS categories. However, there are many cases where it is clear that a story should belong to one or more TOPICS categories, but for some reason the category was not assigned. There appear to be certain time intervals where large numbers of such stories are concentrated, suggesting that some parts of the data set were simply not indexed, or not indexed for some categories or category sets. Also, in a few cases, the indexer clearly meant to assign TOPICS categories, but put them in the wrong field. These cases have been corrected in the Reuters-21578 data, yielding stories that have TOPICS categories, but where TOPICS="NO", because the the category was not assigned in the raw version of the data. "BYPASS" stories clearly were not indexed, and so are useful only for general distributional information on the language used in the documents. 2. LEWISSPLIT : The possible values are TRAINING, TEST, and NOT-USED. TRAINING indicates it was used in the training set in the experiments reported in LEWIS91d (Chapters 9 and 10), LEWIS92b, LEWIS92e, and LEWIS94b. TEST indicates it was used in the test set for those experiments, and NOT-USED means it was not used in those experiments. 3. CGISPLIT : The possible values are TRAINING-SET and PUBLISHED-TESTSET indicating whether the document was in the training set or the test set for the experiments reported in HAYES89 and HAYES90b. 4. OLDID : The identification number (ID) the story had in the Reuters-22173 collection. 5. NEWID : The identification number (ID) the story has in the Reuters-21578, Distribution 1.0 collection. These IDs are assigned to the stories in chronological order. In addition, some REUTERS tags have a sixth attribute, CSECS, which can be ignored. The use of these attributes is critical to allowing comparability between different studies with the collection, and is discussed further in Section VIII. VI.B. Document-Internal Tags Just as the <REUTERS> and </REUTERS> tags serve to delimit documents within a file, other tags are used to delimit elements within a document. We discuss these in the order in which they typically appear, though the exact order should not be relied upon in processing. In some cases, additional tags occur within an element delimited by these top level document-internal tags. These are discussed in this section as well. We specify below whether each open/close tag pair is used exactly once (ONCE) per a story, or a variable (VARIABLE) number of times (possibly zero). In many cases the start tag of a pair appears only at the beginning of a line, with the corresponding end tag always appearing at the end of the same line. When this is the case, we indicate it with the notation "SAMELINE" below, as an aid to those processing the files without SGML tools. 1. <DATE>, </DATE> [ONCE, SAMELINE]: Encloses the date and time of the document, possibly followed by some non-date noise material. 2. <MKNOTE>, </MKNOTE> [VARIABLE] : Notes on certain hand corrections that were done to the original Reuters corpus by Steve Finch. 3. <TOPICS>, </TOPICS> [ONCE, SAMELINE]: Encloses the list of TOPICS categories, if any, for the document. If TOPICS categories are present, each will be delimited by the tags <D> and </D>. 4. <PLACES>, </PLACES> [ONCE, SAMELINE]: Same as <TOPICS> but for PLACES categories. 5. <PEOPLE>, </PEOPLE> [ONCE, SAMELINE]: Same as <TOPICS> but for PEOPLE categories. 6. <ORGS>, </ORGS> [ONCE, SAMELINE]: Same as <TOPICS> but for ORGS categories. 7. <EXCHANGES>, </EXCHANGES> [ONCE, SAMELINE]: Same as <TOPICS> but for EXCHANGES categories. 8. <COMPANIES>, </COMPANIES> [ONCE, SAMELINE]: These tags always appear adjacent to each other, since there are no COMPANIES categories assigned in the collection. 9. <UNKNOWN>, </UNKNOWN> [VARIABLE]: These tags bracket control characters and other noisy and/or somewhat mysterious material in the Reuters stories. 10. <TEXT>, </TEXT> [ONCE]: We have attempted to delimit all the textual material of each story between a pair of these tags. Some control characters and other "junk" material may also be included. The whitespace structure of the text has been preserved. The <TEXT> tag has the following attribute: a. TYPE: This has one of three values: NORM, BRIEF, and UNPROC. NORM is the default value and indicates that the text of the story had a normal structure. In this case the TEXT tag appears simply as <TEXT>. The tag appears as <TEXT TYPE="BRIEF"> when the story is a short one or two line note. The tags appears as <TEXT TYPE="UNPROC"> when the format of the story is unusual in some fashion that limited our ability to further structure it. The following tags optionally delimit elements inside the TEXT element. Not all stories will have these tags: a. <AUTHOR>, </AUTHOR> : Author of the story. b. <DATELINE>, </DATELINE> : Location the story originated from, and day of the year. c. <TITLE>, </TITLE> : Title of the story. We have attempted to capture the text of stories with TYPE="BRIEF" within a <TITLE> element. d. <BODY>, </BODY> : The main text of the story. VII. Categories A test collection for text categorization contains, at minimum, a set of texts and, for each text, a specification of what categories that text belongs to. For the Reuters-21578 collection the documents are Reuters newswire stories, and the categories are five different sets of content related categories. For each document, a human indexer decided which categories from which sets that document belonged to. The category sets are as follows: Number of Number of Categories Number of Categories Category Set Categories w/ 1+ Occurrences w/ 20+ Occurrences ************ ********** ******************** ******************** EXCHANGES 39 32 7 ORGS 56 32 9 PEOPLE 267 114 15 PLACES 175 147 60 TOPICS 135 120 57 The TOPICS categories are economic subject categories. Examples include "coconut", "gold", "inventories", and "money-supply". This set of categories is the one that has been used in almost all previous research with the Reuters data. HAYES90b discusses some examples of the policies (not always obvious) used by the human indexers in deciding whether a document belonged to a particular TOPIC category. The EXCHANGES, ORGS, PEOPLE, and PLACES categories correspond to named entities of the specified type. Examples include "nasdaq" (EXCHANGES), "gatt" (ORGS), "perez-de-cuellar" (PEOPLE), and "australia" (PLACES). Typically a document assigned to a category from one of these sets explicitly includes some form of the category name in the document's text. (Something which is usually not true for TOPICS categories.) However, not all documents containing a named entity corresponding to the category name are assigned to these category, since the entity was required to be a focus of the news story [HAYES90b]. Thus these proper name categories are not as simple to assign correctly as might be thought. Reuters-21578, Distribution 1.0 includes five files (all-exchanges-strings.lc.txt, all-orgs-strings.lc.txt, all-people-strings.lc.txt, all-places-strings.lc.txt, and all-topics-strings.lc.txt) which list the names of *all* legal categories in each set. A sixth file, cat-descriptions_120396.txt gives some additional information on the category sets. Note that a sixth category field, COMPANIES, was present in the original Reuters materials distributed by Carnegie Group, but no company information was actually included in these fields. In the Reuters-21578 collection this field is always empty. In the table above we note how many categories appear in at least 1 of the 21,578 documents in the collection, and how many appear at least 20 of the documents. Many categories appear in no documents, but we encourage researchers to include these categories when evaluating the effectiveness of their categorization system. Additional details of the documents, categories, and corpus preparation process appear in LEWIS92b, and at greater length in Section 8.1 of LEWIS91d. VIII. Using Reuters-21578 for Text Categorization Research In testing a method for text categorization it is important that knowledge of the nature of the test data not unduly influence the development of the system, or the performance obtained will be unrealistically high. One way of dealing with this is to divide a set of data into two subsets: a training set and a test set. An experimenter then develops a categorization system by automated training on the training set only, and/or by human knowledge engineering based on examination of the training set only. The categorization system is then tested on the previously unexamined test set. A number of variations on this basic theme are possible---see WEISS91 for a good discussion. Effectiveness results can only be compared between studies that the same training and test set (or that use cross-validation procedures). One problem with the Reuters-22173 collection was that the ambiguity of formatting and annotation led different researchers to use different training/test divisions. This was particularly problematic when researchers attempted to remove documents that "had no TOPICS", as there were several definitions of what this meant. To eliminate these ambiguities from the Reuters-21578 collection we specify exactly which articles are in each of the recommended training sets and test sets by specifying the values those articles will have on the TOPICS, LEWISSPLIT, and CGISPLIT attributes of the REUTERS tags. We strongly encourage that all studies on Reuters-21578 use one of the following training test divisions (or use multiple random splits, e.g. cross-validation): VIII.A. The Modified Lewis ("ModLewis") Split: Training Set (13,625 docs): LEWISSPLIT="TRAIN"; TOPICS="YES" or "NO" Test Set (6,188 docs): LEWISSPLIT="TEST"; TOPICS="YES" or "NO" Unused (1,765): LEWISSPLIT="NOT-USED" or TOPICS="BYPASS" This replaces the 14704/6746 split (723 unused) of the Reuters-22173 collection, which was used in LEWIS91d (Chapters 9 and 10), LEWIS92b, LEWIS92c, LEWIS92e, and LEWIS94b. Note the following: 1. The duplicate documents removed in forming Reuters-21578 are of course not present. 2. The documents with TOPICS="BYPASS" are not used, since subsequent analysis strongly indicates that they were not categorized by the indexers. 3. The 1,765 unused documents should not be tested on and should not be used for supervised learning. However, they may useful as additional information on the statistical distribution of words, phrases, and other features that might used to predict categories. This split assigns documents from April 7, 1987 and before to the training set, and documents from April 8, 1987 and after to the test set. WARNING: Given the many changes in going from Reuters-22173 to Reuters-21578, including correction of many typographical errors in category labels, results on the ModLewis split cannot be compared with any published results on the Reuters-22173 collection! VIII.B. The Modified Apte ("ModApte") Split : Training Set (9,603 docs): LEWISSPLIT="TRAIN"; TOPICS="YES" Test Set (3,299 docs): LEWISSPLIT="TEST"; TOPICS="YES" Unused (8,676 docs): LEWISSPLIT="NOT-USED"; TOPICS="YES" or TOPICS="NO" or TOPICS="BYPASS" This replaces the 10645/3672 split (7,856 not used) of the Reuters-22173 collection. These are our best approximation to the training and test splits used in APTE94 and APTE94b. Note the following: 1. As with the ModLewis, those documents removed in forming Reuters-21578 are not present, and BYPASS documents are not used. 2. The intent in APTE94 and APTE94b was to use the Lewis split, but restrict it to documents with at least one TOPICS categories. However, but it was not clear exactly what Apte, et al meant by having at least one TOPICS category (e.g. how was "bypass" treated, whether this was before or after any fixing of typographical errors, etc.). We have encoded our interpretation in the TOPICS attribute. ***Note that, as discussed above, some TOPICS="YES" stories have no TOPICS categories, and a few TOPICS="NO" stories have TOPICS categories. These facts are irrelevant to the definition of the split.*** If you are using a learning algorithm that requires each training document to have at least TOPICS category, you can screen out the training documents with no TOPICS categories. Please do NOT screen out any of the 3,299 documents - that will make your results incomparable with other studies. 3. As with ModLewis, it may be desirable to use the 8,676 Unused documents for gathering statistical information about feature distribution. As with ModLewis, this split assigns documents from April 7, 1987 and before to the training set, and documents from April 8, 1987 and after to the test set. The difference is that only documents with at least one TOPICS category are used. The rationale for this restriction is that while some documents lack TOPICS categories because no TOPICS apply (i.e. the document is a true negative example for all TOPICS categories), it appears that others simply were never assigned TOPICS categories by the indexers. (Unfortunately, the amount of time that has passed since the collection was created has made it difficult to establish exactly what went on during the indexing.) WARNING: Given the many changes in going from Reuters-22173 to Reuters-21578, including correction of many typographical errors in category labels, results on the ModApte split cannot be compared with any published results on the Reuters-22173 collection! VIII.C. The Modified Hayes ("ModHayes") Split: Training Set (20856 docs): CGISPLIT="TRAINING-SET" Test Set (722 docs): CGISPLIT="PUBLISHED-TESTSET" Unused (0 docs) This is the best approximation we have to the training and test splits used in HAYES89, HAYES90b, and Chapter 8 of LEWIS91d. It replaces the 21450/723 split of the Reuters-22173 collection. Note the following: 1. As with the other splits, the duplicate documents removed in forming Reuters-21578 are not present. 2. "Training" in HAYES89 and HAYES90b was actually done by human beings looking at the documents and writing categorization rules. We can not be sure which of the document files were actually looked at. 3. We specify that the BYPASS stories and the TOPICS=NO stories are part of the training set, since they were used during manual knowledge engineering in the original Hayes experiments. That does not mean researchers are obliged to give these stories to, for instance, a supervised learning algorithm. As mentioned in the other splits, they may be more useful for getting distributional information about features. There are a number of problems with the ModHayes split that make it less than desirable for text categorization research, including unusual distribution of categories, pairs of near-duplicate documents, and chronological burstiness. (See [LEWIS90b, Ch. 8] for more details.) Despite these problems, this split is of interest because it provides the ability to compare results with those of the CONSTRUE system [HAYES89, HAYES90b]. Comparison of results on the ModHayes split with previously published results on the original Hayes split in HAYES89 and HAYES90b (and LEWIS90b, Ch. 8) is possible, though the following points should be taken into account: 1. The testset we provide in the ModHayes split has one fewer document than the one Hayes used. The document that was removed (OLDID="22026") was a timestamp duplicate of the document with OLDID="22027" and NEWID="13234". So in computing effectiveness measures for comparison with HAYES89/90b, the document with NEWID="13234" should be counted twice. 2. The documents in the Hayes testset had relatively few errors and anomalies in their categorization. And the errors which we did find and correct appear unlikely to have affected the original Hayes results. In particular, it appears that the only errors in the TOPICS field were the addition of a few invalid categories that were not evaluated on. However, for completeness we list the changes in the Hayes testset documents made going from Reuters-22173 to Reuters-21578 (all documents are referred to by their NEWID): Removal of invalid TOPIC "loan" : 13234, 16946, 17111, 17112, 17207, 17217, 17228, 17234, 17271, 17310 Removal of invalid TOPIC "gbond" : 17138, 17260 Removal of invalid TOPIC "tbill" : 17258 Removal of invalid TOPIC "cbond" : 17024 Removal of invalid TOPIC "fbond" : 17087 Correction of invalid PEOPLE mancera to mancera-aguayo: 17142, 17149, 17154, 17177, 17187 Correction of invalid PEOPLE andriesssen to andriessen : 17366 Correction of invalid PLACES "ivory" and "coast" to single correct PLACE "ivory-coast": 18383 3. The effectiveness measures used in HAYES89 and HAYES90b were somewhat nonstandard. See Ch. 8 of LEWIS91d for a discussion. VIII.D. Other Splits We strongly encourage researchers to use one (or more) of the above splits for their experiments (or use cross-validation on one of the sets of documents defined in the above splits). We recommend the Modified Apte ("ModApte") Split for research on predicting the TOPICS field, since the evidence is that a significant number of documents that should have TOPICS do not. The ModLewis split can be used if the researcher has a strong need to test the ability of a system to deal with examples belonging to no category. While it is likely that some of these examples should indeed belong to a category, the ModLewis split is at least better than the corresponding split from Reuters-22173, in that it eliminates the "bypass" stories. We in particular encourage you to resist the following temptations: 1. Defining new splits based on whether or not the documents actually have any TOPICS categories. (See the discussion of the ModApte split.) 2. Testing your system only on the "easy" categories. This is a temptation we have succumbed to in the past, but will resist in the future. Yes, we know that some of the 135 TOPICS categories have few or no positive training examples or few or no positive test examples or both. Yes, purely supervised learning systems will do very badly on these categories. Knowledge-based systems, on the other hand, might do well on them, while doing poorly in comparison with supervised learning on categories with lots of positive examples. These comparisons are of great interest. Of course, it's of great interest to *in addition* analyze subsets of categories (e.g. lots of positive examples vs. few positive examples, etc.). Note that one strategy we considered and rejected is to assume that documents which have no TOPICS but do have categories in other fields (PLACES, etc.) could be assumed to belong to no TOPICS categories. This does not appear to be a safe assumption - we have found a number of examples of documents with PLACES but no TOPICS when there are TOPICS that clearly apply. IX. Feature Sets in Text Categorization For many text categorization methods, particularly those using statistical classification techniques, it is convenient to represent documents not as a sequence of characters, but rather as a tuple of numeric or binary feature values. For instance, the value of feature Fi for a document Dj might be 1 if the string of characters "financial" occurred in the document with whitespace on either side, and 0 otherwise. Or the value of Fi for Dj might be the number of occurrences of "financial" in document Dj. In information retrieval such features are often called "indexing terms" and one often speaks of a term being "present" in a document, to mean that the feature takes on a non-default value. (Usually, but not always, any value but 0 is non-default.) Comparisons between text categorization methods that represent documents as feature tuples are aided by ensuring that the same tuple representation is used with all methods, thus avoiding conflating differences in feature extraction with differences in, say, machine learning methods. For that reason, the Reuters-22173 distribution included not only the formatted text of the Reuters stories, but also feature tuple representations of the stories in each of two feature sets, one based on words and one based on noun phrases. Surprisingly, almost no use was made of these files by other researchers, so we have not included files of this sort in the Reuters-21578 distribution. However, we are willing to make available as part of the distribution any tuple representations of this sort that researchers want to contribute. (Contact [email protected] if you would like to do this.) Perhaps the ideal situation would be if someone with a strong interest in feature set formation produced tuples based on a high quality set of features which other researchers interested only in learning algorithms could make use of. X. Bibliography [This needs to be updated.] @article{APTE94 ,author = "Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss" ,title = "Automated Learning of Decision Rules for Text Categorization" ,journal = "ACM Transactions on Information Systems" ,year = 1994 , note = "To appear." } @inproceedings{APTE94b ,author = "Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss" ,title = "Toward Language Independent Automated Learning of Text Categorization Models" ,booktitle = sigir94 ,year = 1994 ,note = "To appear." } @inproceedings{HAYES89 ,author = "Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and Linda M. Schmandt" ,title = "{TCS}: A Shell for Content-Based Text Categorization" ,booktitle = "IEEE Conference on Artificial Intelligence Applications" ,year = 1990 } @inproceedings{HAYES90b ,author = "Philip J. Hayes and Steven P. Weinstein" ,title = "{CONSTRUE/TIS:} A System for Content-Based Indexing of a Database of News Stories" ,booktitle = "Second Annual Conference on Innovative Applications of Artificial Intelligence" ,year = 1990 } @incollection{HAYES92 ,author = "Philip J. Hayes" ,title = "Intelligent High-Volume Text Processing using Shallow, Domain-Specific Techniques" ,booktitle = "Text-Based Intelligent Systems" ,publisher = "Lawrence Erlbaum" ,address = "Hillsdale, NJ" ,year = 1992 ,editor = "Paul S. Jacobs" } @inproceedings{LEWIS91c ,author = "David D. Lewis" ,title = "Evaluating Text Categorization" ,booktitle = "Proceedings of Speech and Natural Language Workshop" ,year = 1991 ,month = feb ,organization = "Defense Advanced Research Projects Agency" ,publisher = "Morgan Kaufmann" ,pages = "312--318" } @phdthesis{LEWIS91d ,author = "David Dolan Lewis" ,title = "Representation and Learning in Information Retrieval" ,school = "Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003" ,year = 1992 ,note = "Technical Report 91--93." } @inproceedings{LEWIS91e ,author = "David D. Lewis" ,title = "Data Extraction as Text Categorization: An Experiment with the {MUC-3} Corpus" ,booktitle = "Proceedings of the Third Message Understanding Evaluation and Conference" ,year = 1991 ,month = may ,organization = "Defense Advanced Research Projects Agency" ,publisher = "Morgan Kaufmann" ,address = "Los Altos, CA" } @inproceedings{LEWIS92b ,author = "David D. Lewis" ,title = "An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task" ,booktitle = "Fifteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval" ,year = 1992 ,pages = "37--50" } @inproceedings{LEWIS92d ,author = "David D. Lewis and Richard M. Tong" ,title = "Text Filtering in {MUC-3} and {MUC-4}" ,booktitle = "Proceedings of the Fourth Message Understanding Conference ({MUC-4})" ,year = 1992 ,month = jun ,organization = "Defense Advanced Research Projects Agency" ,publisher = "Morgan Kaufmann" ,address = "Los Altos, CA" } @inproceedings{LEWIS92e ,author = "David D. Lewis" ,title = "Feature Selection and Feature Extraction for Text Categorization" ,booktitle = "Proceedings of Speech and Natural Language Workshop" ,year = 1992 ,month = feb ,organization = "Defense Advanced Research Projects Agency" ,publisher = "Morgan Kaufmann" ,pages = "212--217" } @inproceedings{LEWIS94b ,author = "David D. Lewis and Marc Ringuette" ,title = "A Comparison of Two Learning Algorithms for Text Categorization" ,booktitle = "Symposium on Document Analysis and Information Retrieval" ,year = 1994 ,organization = "ISRI; Univ. of Nevada, Las Vegas" ,address = "Las Vegas, NV" ,month = apr ,pages = "81--93" } @article{LEWIS94d , author = "David D. Lewis and Philip J. Hayes" , title = "Guest Editorial" , journal = "ACM Transactions on Information Systems" , year = 1994 , volume = 12 , number = 3 , pages = "231" , month = jul } @article{SPARCKJONES76 ,author = "K. {Sparck Jones} and C. J. {van Rijsbergen}" ,title = "Information Retrieval Test Collections" ,journal = "Journal of Documentation" ,year = 1976 ,volume = 32 ,number = 1 ,pages = "59--75" } @book{WEISS91 ,author = "Sholom M. Weiss and Casimir A. Kulikowski" ,title = "Computer Systems That Learn" ,publisher = "Morgan Kaufmann" ,year = 1991 ,address = "San Mateo, CA" }
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