Textual Statistics and Information Discovery: Using Co
Contents
1. Co occurrences as a unit of analysis Co occurrences are one of several units of analysis in textual statistics As stated above a co occurrence is two words or more that appear at the same time in the same predetermined span of text This analysis allows for the precise description of the lexical environment of a pivot type or pivot word A hypergeometric model below is applied to calculate the lexical associations of a pivot type in which several variables are left to the end user 17 First the co frequency of two associations must be determined this frequency indicates the lowest number of times two types appear together in the corpus in the defined context When no pivot type is available repeated segments two tokens or more appearing together 15 can be used to discover co occurrences with a specified frequency Second a threshold is provided designating the probability level that co occurrence relationship must have for appearing in the predefined context 15 What results is a list or network of co occurring types that can be interpreted through the following Frequency the total frequency of the co occurrence in the corpus Co Frequency the frequency with which the co occurrence appears with the pivot type in the defined context Specificness the degree of probability that the co occurrence will appear in that context Number of contexts the number of contexts that the co occurrence and pivot type appear together in T2. calculations Figure 6 shows the relative frequency for hewlett over the course of 2002 It seems clear from this figure that some kind of activity is taking place from January 2002 to May 2002 after which the relative number of tokens drops significantly This figure is also comparable to the total frequency of this entity and the number of polyco occurrences found each month Figure 5 shows the number of articles gt The type packard showed the very similar results per month shedding light on the difference between the number of articles mentioning HP and the number of tokens the entity effectively has Though the number of articles increases from January to May a comparable increase can be observed for November Given this notable rise in articles when observing the relative or total frequency of hewlett in the corpus the gap is much larger for the number of tokens than the number of articles This indicator may therefore not be a reliable source of information on the real importance of an event 30 25 20 15 Number of FP Articles Number of HP Coocs 10 123 4 5 6 7 8 9 10 11 12 Figure 5 Number of articles vs coocs per month for hewlett Figure 6 Hewlett relative frequency per month Another significant observation is the number of co occurrences per month As shown in the figures 5 and 7 the peaks in polyco occurrences occur in April whereas for both article and
3. for this research Secondly the qualitative analysis of the resulting co occurrence networks showed a great deal of vocabulary actually categorizing the NES placing them in a specific domain For the IT industry the co occurrences were similar among the pivot types what was interesting were those NEs that did not have co occurrences related to their general fields These unexpected associative relationships generally corresponded to events The chronological qualitative analysis revealed pertinent lexical networks not only alerting us to the merger of HP Compaq but also to the role the founders and more specifically Walter Hewlett played in how the merger unfolded The proxy battle and Deutsche Bank relationships appeared between March and April at the high point of the push to merge both companies These lexical networks give interesting insight to how the NYT covered the merger bringing to light key elements of the voting process In the comparison with Luxid these elements were overlooked by the application as they were not part of the predetermined scenario for a merger The extraction or qualitative annotations used by Luxid do bring to the forefront important static information in the text corresponding to pre coded patterns As mentioned before this information void of any statistical weight will not appear using textual statistic methods However unexpected events or even information related to an event such as dissident found
4. media the more likely it is to correspond to some kind of business event proved not to be an easy assumption The ratio co occurrences frequency is not significant enough to conclude that the NE is involved in a potential event in the corpus NYT It would be difficult to present this ratio for the entire table of NEs The average ratio for each of the 76NEs was 3 4 Here we chose to present only NEs corresponding to the information technology industry in Table 2 Microsoft has the highest ratio followed by Google the other information technology industry NEs have a ratio of roughly 2 This information does not seem sufficient to come to any clear conclusions on the hypothesis that the higher the number of co occurrences per frequency denotes an event If the actual co occurrence networks of these NEs are compared the implication of Google Dell Apple Cisco Intel Oracle in an event is not clear Hewlett Packard HP and Xerox however show a great deal of vocabulary that does not correspond to what could be expected from an information technology company 1 Hewlett fight dissident founders merger 11 Xerox kpmg restate accounting investigation As can be seen in the figure 3 below Intel for example has a higher ratio than HP but does not contain any vocabulary that could denote an event This appeared to be the case for other 76NEs as well Wellpoint for instance had a ratio of 9 2 but only co occurrences of competitors o
5. method hopes to reveal these lexical footprints therefore discovering new information that may otherwise go unnoticed by standard mining techniques 1 Introduction It s no scoop that data or the quiet revolution as Bollier 2 puts it has grown tremendously since the availability of computing and databases even more so since the dawn of the internet Some reports even state that the amount of digital content on the web is close to five hundred billion gigabytes up from the estimated three hundred billion gigabytes in 2007 2 25 Data is not just conveniently stored in structured databases it comes in the form of natural language articles blogs forums are among some of the many formats in the mobile network for sharing information Today metadata is widely used to preserve information date author subject key words among others on digital media However with the advent of the semantic web access to natural language data remains as much a challenge today as it was when these analyses became heavily commercialized in the 1990 s One of the more popular goals is the detection and extraction of current events in large compilations of text such as the online media The race for information extraction is on with an increase in the number of open source extraction tools available on the web Although various systems exist yielding impressive results most of them fail to take into account the context of the extractions the
6. mining without considering the specific nature of their data showing how it is possible to use the methods of extraction sequences to identify new trends in a database 16 Today there are many natural language mining techniques machine learning and information extraction through automatic semantic morpho syntactic patterns to name just a couple as discussed during the Message Understanding Conferences MUC 11 The units of analysis used by these techniques rarely go beyond the sentence level and sometimes fail to consider their object of analysis the text as a component in and of itself Here we chose to shift the focus from the sentence level to the text level by applying existing statistical strategies to discover patterns at the text level in a corpus of textual data 2 2 Searching for information entities relationships and events 2 2 1 Named Entities Information Extraction systems have long attempted to group textual elements into Named Entities and relationships or template scenarios between these entities 11 22 Named Entity Recognition NER and Relation Templates continue to be hot topics today as they were during the MUCs which can be noted by the number of open source technologies that have begun to undertake this task The definitions attributed to what are called entities and relationships remain unsatisfactory Entities are roughly defined as names of people organizations and geographic locations in a text 10 11 They a
7. rouge 50 lt Sp micro mag grove iis D e Maa re 1g P DAEN 340 2 34 24 11 31 l E53 og6 itanium 3421 2a LY personal F 51 146 ES Sa 24 22 01 22 las 22 SH OISE EE desim er zamen E 076 Bry ard A P technology microprocessor A pentium Figure 3 graph of Intel cooccurences Observations in a study on automatic disambiguation of Proper Nouns PN described clauses immediately following the PN as having a certain number of categorizing types or pronouns semantically marked allowing for the identification of the PN referent as belonging to a semantic class 29 The types found with the co occurrence analysis are similar in that they do define the NE pivot type as belonging to a semantic class within the scope of the corpus Without attempting to provide a complete semantic or referential study of the NE here this information is important to shed light the observed contrast between both co occurrence graphs intel and hewlett The latter in figure 4 shows few categorizing types whereas intel and others have comparable co occurrence networks as can be seen in Table 3 Shared vocabulary is listed in bold in the table below Table 3 Co occurrence for intel dell and apple Intel Dell Apple chips microprocessor computer imac micro grove pe macintosh corporation quarter computers computer hewlett dell hewlett desktop microprocessors computers packard ipod p
8. 9 25 SAND J Information Overload How April 2009 p 192 196 26 SANDHAUS E The New York Times Annotated Corpus Philadelphia Linguistic Data Consortium 2008 27 TUFFERY S Data mining et statistique d cisionnelle l intelligence des donn es Paris Editions Technip 2007 28 VEINARD M La nomination d un v nement dans la presse quotidienne nationale Une tude s mantique et discursive la guerre en Afghanistan et le conflit des intermittents dans le Monde et le Figaro Th se pour le doctorat en Sciences du Langage Universit de la Sorbonne nouvelle Paris 3 2007 29 VICENTE M R La glose comme outil de d sambiguisation r f rentielle des noms propres purs Corela Num ros Sp ciaux le traitement lexicographique des noms propres 2005 30 WRIGHT K Using Open Source Common Sense Reasoning Tools in Text Mining Research the International Journal of Applied Management and Technology 2006 vol 4 n 2 p 349 387
9. Information Extraction systems 11 27 30 rather they are directly linked to the corpus and will only give information about the corpus in which they appear Furthermore in trying to identify an event it must be noted that it is more than a self contained expression 28 An event is built up of a network of other references either in the same article or a series of articles 1 This research is based on the seven characteristics of an event in narrative texts as defined by Adam 1 and Cicurel 3 e Event core description of the event by its protagonists described by journalists or explained by scientists e Past events other events of the same nature the current event is therefore compared to past events e The context general atmosphere in which the event took place e The periodicity of the event core reproducibility of the event e The background or comments explanation of the event e Verbal reactions reactions to the event by a variety of speakers victims experts representatives etc e Similar stories stories not directly linked to the event but having to do with the general atmosphere associated to the event example after September 11th articles discussing studies on panic and fear Each of these characteristics can give rise to any number of individual articles or can be discussed within the same article This model shows how events are discussed and related by the written press as a network of intricate
10. M PIATESTKY G SMYTH P amp UTHURUSAMY R Advances in Knowledge Discovery and Data Mining AAAI MIT Press 1996 6 FELDMAN R amp DAGAN I Knowledge discovery from textual databases In Proceedings of the International Conference on Knowledge Discovery from DataBases pages 112 117 1995 7 FILLMORE C J Frame semantics and the nature of language Annals of the New York Academy of Sciences Conference on the Origin and Development of Language and Speech 1976 Volume 280 p 20 32 8 FIRTH J R A Synopsis of Linguistic Theory 1930 1955 Linguistic Analysis Philological Society Oxford 1957 9 FLEURY S Le M tier Textom trique Le Trameur Manuel d utilisation Universtiy Paris 3 Centre de Textom trie 2007 10 GRISHMAN R Information Extraction The Oxford Handbook of Computational Linguistics R Mitkov Oxford Oxford University Press 2003 p 545 559 11 GRISHMAN R amp SUNDHEIM B Message Understanding Conference 6 A Brief History Proceedings of the 16th International Conference on Computational Linguistics COLING I Kopenhagen 1996 p 466 471 12 HABERT B NAZARENKNO A SALEM A Les linguistiques de corpus Paris Armand Colin Masson 1997 13 KODRATOFF Y Knowledge discovery in texts A definition and applications Proceedings of the International Symposium on Methodologies for Intelligent Systems 1999 volume LNAI 1609 p 16 29 14 KRIEG PLANQUE A La notion de formule en analy
11. Textual Statistics and Information Discovery Using Co occurrences to Detect Events Erin MACMURRAY Liangcai SHEN erin macmurray temis com lionel shen univ paris3 fr TEMIS 164 rue de Rivoli 75001 Paris France SYLED Universit Paris 3 19 rue des Bernardins 75005 Paris France Mots clefs Statistiques textuelles fouille textuelle co occurrences d tection d v nements Keywords Textual statistics text mining co occurrences event detection Palabras clave Estadisticas texuales b squeda textual co occurrencias detecci n de eventos Abstract One of the major shortcomings of Text Mining systems is in their failure to relate extracted information to the greater context in which a text was produced defining with difficulty an event as actually corresponding to a real world object An event is made up of a complex network of references leaving lexical footprints in the text Whereas more traditional text mining techniques use predetermined qualitative annotations to formulate interpretations about events textual statistics uses quantitative textual information to come to qualitative conclusions The objective of this paper is therefore to test textual statistics as a means for mining and more specifically using co occurrence calculations to detect statistically significant events In analyzing the New York Times annotated corpus with the co occurrences of known named entities this
12. al 200 NE were retained for co occurrence analysis Those NEs with 24 tokens or less were also removed from the list due to their low frequency that would not produce results on a corpus of this size Each NE from this list was then put as pivot type in the Trameur co occurrence option A co frequency of 10 and a threshold of 20 were used in the context of the sentence in other words the boundary of the punctuation mark period These criteria were set at high levels in order to keep the resulting co occurrence graphs legible without losing too much information A stop list of common English words was also used so as to avoid taking them into account in the analysis removing a potential source for noise In order to test the first hypothesis a number of co occurrences frequency of pivot type ratio was calculated number of co occurrences of the pivot type r 100 total frequency of the pivot type The higher the ratio the more chance a prominent event may have of taking place The second hypothesis was tested through a qualitative analysis of the resulting co occurrence and polyco occurrence graphs In order to then follow an event as it unfolds month by month a subcorpus was compiled containing all the articles mentioning hewlett The smaller subcorpus allows for a more manageable size in analyzing polyco occurrences of a single event in the Trameur Accordingly the co frequency and threshold were lowered to 5 and 10 for the reasons men
13. ers is not part of the generally coded patterns used for extraction This information that is not determined by a conceptual model will not be detected by such information extraction techniques Textual statistics a more dynamic approach to the text helped shed light on associative relationships NEs were involved in Though not all these relationships corresponded to an event of interest they did produce lexical networks summarizing how the NEs were discussed in the NYT Such calculations could help define and evaluate current information extraction systems through comparing both quantitative chronological fluctuations of relationships and qualitative lexical networks results In conclusion if we consider NEs as dynamic units that are susceptible to chronological change textual statistics as we have observed is an appropriate means of following such evolutions 6 References 1 ADAM J M Unit s r dactionnelles et genres discursifs cadre g n ral pour une approche de la presse crite Pratiques n 94 1997 2 BOLLIER D The Promise and Peril of Big Data Washington DC The Aspen Institute 2010 3 CICUREL F Les sc narios d information dans la presse quotidienne le Fran ais dans le monde num ro sp cial Recherches et applications M dias faits et effets Septembre 1994 4 DAVID B Guerre en Irak Armes de communication massive Informations de guerre en Irak 1991 2003 Paris CNRS Editions 2004 5 FAYYARD U
14. g information on the column where the article is organized the author date and named entities In order to compare results obtained between short and longer periods of time two sub corpora were created for this research The period of 2002 and an extracted subcorpus containing only articles with the type hewlett were selected to follow events of that period The year 2002 was chosen due to the number of articles produced during that year in comparison to other years since 2000 Due to the heterogeneous nature of the data it was clear that for the purposes of a statistical study the corpus would have to be broken down by genre category or in this case by the newspaper column the article belonged to This decision is also useful for comparing results among the different columns predetermined by the NYT in the metadata Selecting articles according to this criterion proved to be more difficult than expected Although the NYT annotations indicate the column their names are not always consistent Likewise more than one column name can be attributed to the same article In order to determine which articles to include in this study the corpus was parsed using an in house PERL program to extract the column name and date From these results we chose to focus only on complete articles excluding summaries of current events with consistent column names throughout the periods of study Here results will be presented for articles corresponding to the Business Fi
15. generic templates often change from one need to the next requiring more or less detail in the concepts they provide However generic conceptual models may be their genericity does not cover enough ground explaining why domain information models are so heavily sought after Being capable of detecting events without the use of a predefined information model is therefore not trivial in business intelligence applications 2 2 3 Events The general objective for text mining systems is defined as detecting pertinent information or pertinent events and linking these events to others occurring in text However determining what exactly pertinent information or an event is in order to arrive at real world conclusions proves to be no easy task As mentioned above one of the major shortcomings of Text Mining systems is in their failure to relate extracted information to the greater context in which a text was produced It is difficult to define an event as actually corresponding to a real world object As discussed in a number of articles ranging from Named Entity Recognition to the discourse analysis of proper nouns the actual designation of events changes with time not only in graphical form but also in meaning 4 14 20 21 23 Likewise as David 4 states the media is subject to an ontological reality that is fickle and unstable Events therefore are not just entities or templates as defined by most
16. he hypergeometric model determines the most probable value according to the following parameters T the number of tokens in the corpus t the number of tokens in the pivot contexts F the frequency of the co occurrence in the corpus f the frequency of the co occurrence in the pivot contexts This unit of analysis seems particularly interesting for detecting associative relationships between words In taking co occurrence analysis one step further it is also possible to calculate polyco occurrences 18 otherwise known as the co occurrences of co occurrences After calculating the network for a given pivot type each resulting co occurrence is then analyzed itself as a pivot type in the same context as the original pivot producing a network of interrelated units figures 8 and 9 section 4 2 These associative relationships help show prominent information that may otherwise go unidentified by qualitative annotations of the corpus 3 Corpus and Analysis 3 1 Collecting data New York Times Annotated Corpus The corpus for this study was taken from the New York Times Annotated Corpus 26 which contains almost every article in the New York Times NYT from January 1st 1987 to June 19th 2007 This corpus uses the News Industry Text Format NITF an XML standard now widely used by the online media The articles are enriched with metadata provided by the New York Times News Room and Indexing service as well as the online production staff givin
17. ion display possible weak signals in the Hewlett subcorpus This question needs further investigation Nevertheless it must be noted that the months of June July August September October and December have very little data to be entirely conclusive in terms of a statistical analysis Table 4 Ratio for hewlett per month 2002 Month Freq Coocs Ratio Month Freq Coocs Ratio January 197 8 4 July 21 1 4 7 February 214 11 5 1 August 48 2 4 1 March 431 19 4 4 September 66 2 3 April 234 20 8 5 October 21 3 14 2 May 159 6 3 7 November 155 8 5 1 June 40 0 0 December 27 0 0 The qualitative analysis also confirms the ratio figures Co occurrences such as merger deal vote appear in the polyco occurrence graphs from January to May Figures 8 and 9 show the polyco occurrences for January and April respectively January figure 8 already displays the disagreement with the founders of HP in their relationship with the co occurrence merger The months that follow are fairly similar in their lexical networks with the exception of March and The frequencies of hewlett in figure 7 have been divided by 100 so that the number of co occurrences and the frequency could be displayed on the same graph April figure 9 where more activity takes place due to the proxy battle with the founders Figure 9 shows the vote to merge also in March along with Deutsche Bank which is involved in the voti
18. ms In this case we chose to compare the month by month HP polyco occurrences to an extraction on the same corpus using the graphs produced by Luxid a TM application by Temis Luxid applies Temis Skill Cartridge SC technology to detect and extract information of interest Here the SC for business intelligence relationships was used for comparative purposes If we consider each polyco occurrence as a relationship a parallel can be drawn between the number of polyco occurrences and the number of Luxid relationships on a monthly basis The quantitative analysis in figure 10 shows a similar trend in the fluctuation of both types of relationships It must be noted that the SC Board relationships were not included in this count as their event status can be disputed It is clear in this figure that activity appears from January to May and from October to November in a very similar manner to the fluctuations of co occurrences 30 25 2 Number of Trameur Coaccurrences co freq 5 15 thresho d 10 i Number of Luxid Relationshios involving HP 0 1 2 a ft 5 6 8 9 10 11 12 Figure 10 Comparison of Luxid Relationships and Co occurrences A closer qualitative look at the resulting graphs for both January and April show that the HP Compag merger is the highlight of this period However Luxid does not display information on the dissident founders nor the Deutsche Bank scandal fig
19. n event It was thus necessary to gather a list of attested NEs for research in this corpus The Fortune 500 list was used for this purpose From the first 200 NEs in the list only non ambiguous NEs were retained Due to tokenization graphical element between two white spaces issues that go along with analyzing raw data co occurrences cannot be calculated on repeated segments NE such as General Electric are therefore considered as two separate tokens general and electric by the Trameur making a distinction between these tokens and their counterparts that are not NE difficult to determine The token ge could therefore be used to search for occurrences of General Electric instead of searching for the ambiguous tokens general and electric separately in the corpus In certain cases an unambiguous acronym could be used to find the NE ge gm amr cbs in other cases the NE was broken down into two tokens with the part being the least ambiguous hewlett berkshire kraft ford Here the when a Named Entity is being referred to capital letters will be used General Electric however when the type or token in the corpus is being discussed lower case letters show the exact way they were written in the corpus ge or general electric used as the pivot type in the co occurrence calculation The degree to which a NE was ambiguous for this corpus was left up to the human tester s discretion After cleaning the Fortune 500 list only 91 of the origin
20. nancial Desk The articles were stripped of their XML metadata except for the month and year of publication and cleaned of upper case distinction They were then saved in a collective file in simple txt format for processing in Lexico 3 24 and the Trameur 9 both textual statistic tools developed by the University Sorbonne Nouvelle Paris 3 800006 25500 7 700000 25000 00000 24500 200006 24000 7 400000 m Number of tokens 23500 E Numberof Tyoes 300000 ee 23000 100000 22500 T ar 22000 1 2 3 4 5 6 7 8 S 10 1112 1 2 3 4 5 6 7 8 1011 12 Figure 1 Number of Tokens in NYT 2002 corpus per month Figure 2 Number of Types in NYT 2002 corpus per month The final cleaned corpus NYT 2002 contains a total of 10 968 articles for 8 059 702 tokens and 71 072 types The number of tokens fluctuates only slightly over each month with July having the highest number of tokens at 758 512 and August the lowest at 631 054 tokens in figure 1 The number of types seems to show greater fluctuation over the year Again July has by far the greatest variety of vocabulary with 25 378 types in figure 2 3 2 Analyzing data methodology and criteria As previously stated co occurrence analysis with the Trameur was selected as a means of detecting events that companies or NEs could be involved in The aim here is to see if using NEs as a pivot type would produce lexical network denoting a
21. ng process scandal discussed earlier After April the polyco occurrence graph displays little information reflecting the fact that HP is covered less by the NYT at that period until November The slight peak in November is due to the resignation of Micheal Capellas as president of the post merger HP Compaq company which quickly left the news explaining the drop in activity for December Poly Cooccurrents Forme annotation 1 Pole hewlett Parties selectionnees month 200201 Co Freq 5 Seuil 10 bleu LIKE 3pe1t orange LE lt dp lt 20 vert 2043750 rouge 503p 110247 610 876 15 38134 25010 8522 1811 4015 24010 1122 S _ 2 7 Figure 8 Hewlett polycoocs for January 2002 1500 0113 Poly Cooccurrents Forme annotation 1 Pole hewlett Parties selectionnees month 200204 Co Freq 5 Seuil 10 bleu 104 5p lt 1 orange 12 lt 3p lt 20 vert 20 lt 3p lt 50 rouge 50 lt 3p 40 10 89 40 10 885 en Da BAIN cone EU ae 18 10 2 18 m eo 20 10 53 20 14 16 11 4h 0010 3810 D SERIE MEL UE a 12 10 08 10 11 16 48 9 iiis 130248013 fax 5 10 21 5 7 10 57 7 150705 1111383011 7 12 46 7 fe Ce Figure 9 Hewlett relative frequency per month 4 3 Comparing Co occurrences to a TM system These polyco occurrence networks can be compared to certain graphs produced by TM syste
22. pe of this article however the textometric strategy considers the text as material on its own Pre analysis categories qualitative coding may result in the mutilation of the textual material 15 This research therefore aims at bypassing qualitative coding when studying textual data by using known methods of textual statistics Although this field is not generally considered a text mining technique by the industrial community it seems an appropriate strategy for discovering related events in a corpus when no predetermined information model is available Textual or lexical statistics use quantitative information to formulate qualitative interpretations 15 Following this definition this method can be included among other text mining strategies Textual statistics consists of seeing the document through a prism of numbers and figures producing information on the frequency counts of words otherwise known as tokens 19 or occurrences 15 The term token will be used in this paper by opposition with type 19 or form 15 which is a single graphical unit corresponding to several instances tokens in the text Another important unit of count is the co occurrence the statistical attraction of two or more words in a given span of text sentence paragraph entire article In comparison with approaches that use qualitative coding textual statistics would have a relatively low maintenance cost due to the minimum amount of actual processing 2 3 2
23. pieces of information Following these arguments two hypotheses can be formulated 1 The NE involved in an event will have a higher frequency and greater number of co occurrences as it is discussed by a series of newspaper articles 2 Events leave lexical footprints in the text that can be revealed using textual statistics by determining what is statistically significant in a given article 2 3 Textual Statistics and co occurrences a mining strategy 2 3 1 Textual statistics As mentioned above using qualitative coding usually in the form of morpho syntactic or semantic annotations as discussed above to drive quantitative conclusions almost defeats the purpose of discovering unknown information in the text This calls into question the accurate interpretation of results acquired using basic information extraction techniques Can there be a bias free interpretation of big data This question also brings to mind current evaluations of TM systems Following MUC guidelines precision and recall remain the gold standards for measuring such systems However one man s noise is another man s data 2 which clearly points out the difficulty in creating a generic system that can objectively process large quantities of data There is no agnostic method of running over data once you touch the data you ve spoiled it 2 To what extent is bad data good for you 2 This being stated processing purely raw data is beyond the sco
24. r drugs were observed nothing that would alert an analyst to a potential event Microsoft on the other hand has a ratio of 7 5 and contains like HP and Xerox a great deal of event vocabulary court settlement sanctions illegally Table 2 Ratio Co occurrences Frequency for Computer Industry NE Company Freq Coocs Ratio Apple 449 11 2 4 Cisco 430 11 2 5 Dell 580 11 1 8 Google 298 12 4 Hewlett Packard 1613 38 2 3 Intel 746 25 3 3 Microsoft 1323 100 7 5 Oracle 194 2 1 Xerox 512 7 1 3 Intel displays in figure 3 what can be called descriptor or categorizing vocabulary It comes as no surprise that this company which produces microchips for PCs has such tokens in its co occurrence network The colors in the following networks figures 3 and 4 correspond to the degree of specificness of the co occurrence from most to least specific red green orange blue The thickness of the relationship denotes the number of contexts the co occurrence shares with the pivot type the more common contexts are found the thicker the line The numbers provided for example figure 3 microprocessor 31 29 correspond to in order of appearance the co frequency specificness and number of shared contexts The double asterix denotes a specificness of 49 or higher Cooccurrents Forme annotation 1 Pole intel bleu 20 lt Sp lt 22 orange 22 lt Sp lt 40 vert 40 lt Sp lt 50
25. re perceived as rigid designators that reference real world objects organized in an ontology 23 However these definitions fail to take into account the semantic complexity of named entities in terms of their surface polysemy and their underlying referentiality which aims at combining both the linguistic designation of an entity and the extra linguistic level or the real world object an entity refers to 23 At this stage our method has yet to provide a satisfactory definition of named entities Given the intricacy of entity modeling we disregard any predefined named entity here after NE categorization 2 2 2 Relationships Relationship templates prove to be even more difficult to define In many cases the literature confuses naturally occurring relationships with domain information models Naturally occurring relationships exist either through a semantic relationship between two words synonym antonym conceptual an ontological relationship hyperonym hyponym meronym or a syntactic relationship predicate argument Most templates try to use a conceptual model for defining a scenario or event For example a predefined scenario may be a person has a position in a company and is starting this job 10 These models are very much like Frame semantics 7 applied in the FrameNet project that uses human annotators to code various predefined scenarios in a corpus Unfortunately for business intelligence applications these
26. rocessor advanced intel microsoft devices microsoft quarter x chip design personal windows pentium processors printers jobs technology personal compaq computers computer packard os semiconductor itanium servers Categorizing terms such as computer or personal and NEs corresponding to competitors microsoft or partners appear These pivot types intel dell and apple all seem linked through their lexical networks Cooccurrents Forme annotation 1 Pole hewlett bleu 20 lt 3p lt 22 orange 22 lt Sp lt 40 wert 40 lt 3p lt 50 rouge 50 lt 3p TT meee favor shareholders founders computer 4 m heir pes 45125 50 Sov 7m DATE printer 6j b Q 4248 A N san 2 ae La ae QUE _ 8 691391 108 36 53 101 ena g boara personal nr SE RSS 313 1 rate WA Le i ee Fes 5 biain a Es EE AEN de fosse Figure 4 graph of hewlett co occurrences computers Though it can be noted that a ratio co occurrences frequency does not necessarily mean an event is taking place for the overall corpus 2002 it may help follow or detect such information on a monthly basis 4 2 A Closer look at the HP Compaq merger The subcorpus Hewlett as discussed in section 3 1 is made up of 200 NYT articles from the original NYT 2002 corpus The type hewlett was used as the pivot type in co occurrence and polyco occurrence
27. se du discours Cadre th orique et m thodologique Besan on Presses Universitaires de Franche Comt 2009 15 LEBART L amp SALEM A Statistique textuelle Paris Dunod 1994 16 LENT B AGRAWAL R amp SRIKANT R Discovering trends in text databases Proceedings KDD 1997 AAAI Press 14 17 p 227 230 17 MARTINEZ W Mise en vidence de rapports synonymiques par la m thode des cooccurrences Actes des 5es Journ es Internationales d Analyse Statistique des Donn es Textuelles Ecole Polytechnique de Lausanne 2000 18 MARTINEZ W Contribution une m thodologie de l analyse des cooccurrences lexicales multiples dans les corpus textuels Th se pour le doctorat en Sciences du Langage Universit de la Sorbonne nouvelle Paris 3 2003 19 MCENERY T amp WILSON A Corpus Linguistics Edinburgh University Press 1996 20 MOIRAND S Les discours de la presse quotidienne observer analyser comprendre Paris Presses Universitaires de France 2007 21 NEE E Ins curit et lections pr sidentielles dans le journal Le Monde Lexicometrica num ro th matique Explorations textuelles S Fleury A Salem 2008 22 POIBEAU T Extraction automatique d information Du texte brut au web s mantique Paris Herm s Sciences 2003 23 POIBEAU T Sur le statut r f rentiel des entit s nomm es Proceedings TALN 05 Dourdan France 2005 24 SALEM A Lexico 3 version 3 6 Paris Lexi amp Co 200
28. tioned earlier 4 Results 4 1 Fortune 500 Named Entities and Co occurrence networks Only 76 of the remaining 91 NEs showed co occurrences in the corpus Those that did not produce results had in general low frequencies for example metlife kbr and pnc had frequencies of 25 32 and 45 respectively From here on out the remaining list of 76 Fortune 500 NEs will be referred to as the TONEs The total frequency for the retained 76NEs is 29 452 corresponding to 388 tokens for each of the 76NEs In using a threshold of 20 or higher the NE had on average 11 co occurrences The highest return for this threshold was 100 co occurrences Microsoft and the lowest frequency of zero excluded was one Alcoa Chevron CVS Wells Fargo Costco Conagra Tyson Rite Aid Staples J C Penney One of our first remarks was the number of new NEs following a rough MUC definition of Person Location or Company that appeared in the co occurrences of each of the original 76NEs Of the average 11 co occurrences four were other NEs this count includes only new NE not co occurrences that correspond to part of the original company for example foods in Conagra Foods is not counted as an NE for the retained 76NEs Conagra The total of 7ONEs that contained a corresponding NE was 60 as shown in Table 1 These NEs corresponded generally to competitors partners or suppliers of the pivot type NE One unexpected case was Xerox which shared a co freq
29. token counts the peak occurs in March A qualitative analysis of the polyco occurrences shows the merger of HP and Compag to be the focus from January to May The actual vote to merge both companies takes place in March however the founder Walter Hewlett sues the company over the voting process which may be an explanation for the peak observed in April In figure 5 the peak due to the merger is definitely present in the number of NYT articles along with the problems caused by the disagreement with the founders 50 45 40 35 30 25 Number of HP Coocs 20 HP Frequency 15 74 10 4 1 2 3 4 5 6 7 8 9 10 11 12 Figure 7 Hewlett Coocs vs Hewlett Frequency per month Below table 4 displays the chronological ratio for the type hewlett The month of April shows a ratio well above those observed over the entire year 2002 On a monthly basis this ratio could alert us to a potential event especially when compared to the ratio of other months in 2002 as well as the results of the polyco occurrence graph figure 9 However October is a major exception in following our hypothesis with a ratio of 14 2 it seems that there should be a major event for this month The actual polyco occurrence graph shows a relationship with depot and computers This can be explained by an article describing a supply deal between HP and Home Depot for providing PCs to Home Depot stores Could this except
30. uency of 37 and a specificness indicator over 49 with KPMG an audit company Table 1 Fortune 500 Entities retained for analysis containing other named entities Named Entity Freq Named Entity Freq Named Entity Named Entity Freq 1 At amp T 1352 16 Dell 580 341 Lockheed Martin 148 46 Qwest Communications 1040 2 Aetna 113 17 Delta 475 32 Lowe 125 47 Sears 309 3121608 40 18 Disney 1297 33 Macy 56 48 Sprint 303 4 Amazon 591 19 Exxon Hg a McDonald 375 49 Squibb 173 5Amgen 105 20 Fannie Mae 155 _35 Medco 136 50 Staples 116 G OMR 91 21 Ford Motors 1928 36 Merck 381 51 Tiaa 70 7 Apple 449 22 Freddie Mac 134 37 Mictospit 1323 52 VAL 283 g Berkshire Hathaway 120 23 General Electric 145 38 Motorola 257 53 UPS 256 g Boeing 552 24 General Motors 37 39 Nike 150 54 US Bancorp 88 10 CBS 1030 25 Goldman Sachs 1054 40 Northrop 226 55 Verizon 445 11 Cigna 144 26 Google 298 41 Oracle 194 56 Viacom 561 12 Cisco 430 27 Halliburton 309 42 Pepsi 440 57 Wal Mart 648 13 Citigroup 1399 28 Hewlett Packard 1613 43 Pfizer 436 58 Warner 2384 14 Coca cola 387 29 Intel 746 44 Phillip Morris 387 59 Wellpoint 65 15 Comcast 339 30 Kraft Foods 75 45 Procter amp Gamble 244 60 Xerox 512 The hypothesis the more a subject is discussed by the
31. ures 11 12 percent said would Sanmjna scr scheduled scheduled A x 4 merger iai i R i Partnership p Apple Computers HESE x Acquisition Acquisitid x Acqui merger declaring report Compaq computer may intended plan to plan Figure 11 Luxid Relationships for HP January 2002 On the other hand the co occurrence calculations will not pick up on info that is directly sought after through SC patterns partnership manpower in the graph figure 11 If the information has no statistical weight for the month analyzed textual statistics will not pick it up K ke nacelle Bell oD planning declared Hewlett justify G said rawyers would chi AB f probably report reported i justify Compaq Computer said rave been cast Tie company declared planning a 4 pae s Stock Information AU ee tion Acquisition expects k ncial Reporting reconmended probably stock Infopmati Merger Merger reported anyers L a x io SR ye x Kre orted reported r might Aad declare Figure 12 Luxid Relationships for HP April 2002 probably 5 Discussion and limits In this paper we used textual statistics more specifically co occurrences as a method for the detection of significant events in the corpus Two approaches quantitative and qualitative were used to analyze the lexical network produced by co occurrence analysis of NEs Firstly as
32. we observed in section 4 1 for the 76NEs tested neither the frequency nor the number of co occurrences was enough information to alert us to an event that the pivot type could be involved in However it would be interesting to perform further qualitative analysis on NEs with higher ratios than usual in order to see how they are discussed by the NYT especially when not related to any specific event The chronological study of hewlett did reveal alarming peaks for the month of April and to a lesser degree November To what extent these figures can be used to alert end users to potential events requires further exploration and testing This current research at least over the span of a year does not show the individual counts of frequency or co occurrences as being sufficient enough for event detection on their own The ratio as we have observed provides interesting contrast on a month to month basis but requires further investigation especially when dealing with very low figures One of the more important limits to this research the identification of NEs remains difficult when using tokens as a unit of search Ambiguous NEs could produce such incoherent results they would be unexploitable for the end user Likewise NEs that are made up of two distinct segments general electric for example present for the moment complications when interpreted as a single pivot type for co occurrence calculations Though work arounds do exist we have not implemented them
33. xical network used to discuss an event by the media From this analysis we attempt to discover knowledge that may otherwise go unnoticed by qualitative annotations used in standard extraction techniques 2 Background 2 1 Big Data Problem and Data Mining Solutions Since the mid 1990s Data Mining has seen a steady growth due to the development of new efficient algorithms that handle large volumes of data in the commercial domain 5 Data Mining will be defined for the purpose of this research as the sum of techniques and strategies used in the exploration and analysis of computerized databases in order to detect rules tendencies associations and patterns in the data The techniques can be either descriptive or exploratory with the goal of bringing to light information that would otherwise be obscured by the sheer quantity of data Alternatively they can be defined as predictive or explanatory aiming at extrapolating new information from the information available 27 Text Mining TM is often described as a subfield of Data Mining with an added challenge of structuring natural language so that standard Data Mining techniques can be applied 13 6 The goals for processing natural language are therefore twofold 1 Structuring free text for use by other computer applications 2 Providing strategies for following the trends and or patterns expressed in the text Early work in text mining tried simply applying the algorithms developed for data
34. y produce The objective of this paper is therefore to test textual statistics as a means for mining information about business economic events in the corpus the New York Times Here we apply one method of monolingual text exploration co occurrences in assisting the identification of events for business and strategic intelligence applications In order to test this strategy a list of entities was gathered from among the Fortune 500 companies The goal here is to use the entity as the pivot type for co occurrence calculations The software Trameur 9 helps calculate and visualize the co occurrence relationship at the article level displaying the pivot type and its associated types as a network In order to return to the context in which a co occurrence relationship was found the corresponding newspaper article can be easily accessed using Trameur function map of sections The results of the co occurrences will constantly be compared to the original newspaper article in which they appear so as to verify the results with their greater context From the Firthean inspiration You shall know a word by the company it keeps 8 we chose to focus on two aspects for event identification 1 Quantitative the importance of named entity frequency in the corpus 2 Qualitative The company or co occurring vocabulary that a named entity effectively has in the corpus Co occurrences are used here as a method for revealing the footprint or le
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