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The interactive graph clearly shows which stories are most discussed. By hovering with the mouse over any of the points, the most typical news article header of that moment in time is shown so that users can get informed of the development of that story.
The system decides on the most typical article header statistically by selecting the medoid, i. By clicking on any of the curves, a new page will open showing the articles that are part of that cluster plus all meta-information available to the system.
This graph thus shows ten trend lines in one graph, for the sake of comparison. Similarly, Figure 6 visualises the numbers of news articles and of Social Media postings over time on four science areas.
The graph shows longer-term developments. The chosen resolution is one day. For each of the four science areas, two trend curves are displayed to facilitate the visual understanding of the relative long-term development.
Such graphs can be rather revealing. Comparing only English language articles in predominantly English-speaking countries UK and Ireland in Europe; graph not shown here with the English language articles in the USA, the difference is smaller, but it still notable 1.
To put these numbers into perspective: the reporting on the reference categories Conflict, Ecology, Society and Sports, considering only the English language, was respectively 2.
Note that, in EMM, sports articles are additionally only taken from general news streams because EMM does not scan sports pages of news sites.
Looking in detail at a specific topic such as Space, we observe that there is a very strong correlation between the peaks, but the volumes are much smaller in the UK and Ireland, compared to the USA See Figure 9.
Other than a weak correlation between product announcements in the media and on twitter, we have not observed a clear media-driven discussion on the social media, i.
Such data is a good starting point for the work of social scientists, who can then search for an interpretation and for explanations. Economists and politicians may then think of possible remedies if needed and wanted.
The graph shows the number of news articles per day in the daily news clusters about the same event or subject. By hovering over any of the bars, the news cluster title is displayed so that users can explore what happened that day.
By clicking on that day, the users are taken to the page with information on that day's news cluster in order to read the articles, see the related meta-information and follow hyperlinks to related reports in other languages.
The graph allows exploring developments over longer periods of time and refreshing one's memory on what happened when. Figure 11 shows the development of positive or negative tonality or sentiment measured in English and French news articles, using a one-week resolution.
Early warning graphs Figure 8 visualises results on the most recent events of a certain type, allowing stakeholders to become aware of the latest developments, to deepen their understanding of what happened by reading the related news articles and to take action, if needed.
The graph called daily alert statistics shows the currently biggest threats world-wide, with decreasing relevance from left to right the red threats are the ones with the highest alert levels.
MedISys counts the number of articles in the last 24 hours for any country-threat combination e. This ratio is then normalised by the number of articles for different days of the week there are less articles on the weekend.
The alert statistics graph then shows the results of all calculations, ranked by the value of this ratio. Note that the ratio is entirely independent of the absolute numbers as it rather measures the unexpectedness.
Each country-threat combination is shown in two columns: the left one light blue shows the observed number of articles while the right one red, yellow or blue shows the expected two-week average.
The same categories for countries and for threats exist for almost all EMM languages, meaning that the articles may be found in one language only e.
Polish or Arabic , which often is different from the languages spoken by the MedISys user. The graph is interactive: Users can click on any of the bars to jump to a new page where all relevant articles for this country-threat combination are displayed, together with a heat map and a trend line showing the development over the past 14 days.
The Spain-legionellosis threat combination in Figure 10 no longer is a top threat as it had already been reported on for four days. Further graph types used in EMM Figure 11 shows a node graph visualising co-occurrence relations between people.
For each person, the most associated entities persons or organisations are displayed. The subset of common entities is highlighted in red.
The graph is interactive: by clicking on any of the entity nodes, they jump to a page with the news mentioning that entity and displaying all automatically extracted meta-information e.
Figure 2 , or to the Wikipedia page for that entity. Further entities can be added to the same graph. EMM-NewsExplorer produces the correlation data by counting which entities are mentioned together with which other entities in the same news items.
In order to suppress media VIPs such as the US president from the purely frequency-based correlation lists called 'related entities' in NewsExplorer , a weighting formula is used that brings those entities to the top that are mostly mentioned together with this person and not so much with other persons.
The data, referred to in NewsExplorer as 'associated entities', is produced on the basis of mention co-occurrence in the news in 21 different languages, i.
EMM recognises direct speech quotations in the news in about twenty different languages and keeps track of who issued the quotation and who is mentioned inside the quotation.
Figure 12 shows a quotation network indicating who mentions whom arrows. Persons most referred to are automatically placed closer to the centre of the graph.
Quotation networks are no longer used in EMM. The same applies to topic maps, which display the most prominent subject matters referred to in a document collection.
The topics are grouped into islands of relatedness using a method known as Kohonen Maps. The more prominent a group of topics is in the collection, the higher the mountains on the island, with peaks being snow-covered.
Summary and conclusions, pitfalls Computers have the ability to sieve through large volumes of data in little time and the technologies required for Automated Content Analysis ACA have matured to a level where automatically produced results can be useful for the human analyst.
We have argued that a man-machine collaboration for the analysis of large volumes of media reports will produce best results because people and computers have complementary strengths.
We have presented the main functionality of the European Commission's family of Europe Media Monitor EMM applications, which currently gathers an average of , online news articles per day from about 5, online news sources in seventy languages and also from social media postings about certain themes , categorises the news into about 2, different categories, groups related articles, extracts various types of information from them, links related articles over time and across languages and presents the analysis results in a variety of ways to the human end user.
Moderation tools support the users in viewing the data, in selecting and amending it and in producing in-house newsletters for the information-seeking decision takers.
Monitoring not only English or some widely spoken languages is important in order to avoid bias and also because the news is complementary across languages, both for contents and for the sentiment contained therein.
Automatic tools that process and analyse documents turn unstructured information into a structured format that can easily be processed by machines and that also provides useful data for the human user.
This results in a data collection, where for each article, we know the news source, the country of origin, the language, the timestamp of the publication, the news categories, the persons, organisations and locations mentioned therein, related articles within the same and across different languages, quotations by and about persons.
Additionally, we have data about trends, i. This structured collection makes it in principle possible to produce any statistics and to establish any trends related to these types of information.
For selected subjects and feature combinations, the JRC regularly publishes its analysis, allowing EMM users to have a deeper insight into the publications on subject areas of their interest.
In this article, we presented a range of different types of analyses and visualisations in order to give an overview of distributions and trends observed during large-scale media analysis.
Such an extraction and aggregation of data is not usually the final objective, but it normally is the starting point for an intellectual human analysis.
Analysts can get inspired by the data, questions may arise, suspicions may get confirmed or contradicted. Used carefully, we believe that the analyses produced by EMM or similar systems can be very useful because they may be used as an inspiration and as empirical evidence for any argument human analysts may want to make.
However, we find it extremely important that users be aware of the limitations and of possible pitfalls when using such data, be it from EMM or from other automatic systems: First of all, media monitoring is not reality monitoring.
What the media say is not necessarily factually true and media attention towards certain subjects usually differs from the real-life distribution of facts or events, giving media consumers a biased view.
Media reporting is heavily influenced by the political or geographical viewpoint of the news source. It is therefore useful to analyse a large, well-balanced set of media sources coming from many different countries world-wide.
EMM aims to reach such a balance, but sources are also added on request of users, it is not always known what political standpoints newspapers have, and not all news sources are freely accessible.
For this reason, EMM displays the list of media sources so that users can form their own opinion. Any analysis, be it automatic or man-made, is error-prone.
This is even true for basic functionalities such as the recognition of person names in documents and the categorisation of texts according to subject domains.
Machines might make simple mistakes easily spottable by human analysts, such as categorising an article as being about the outbreak of communicable diseases when category-defining words such as tuberculosis are found in articles discussing a new song produced by a famous music producer, which is easily spottable by a person.
On the other hand, machines are better at going through very large document collections and they are very consistent in their categorisation while people suffer from inconsistency and they tend to generalise on the basis of the small document collection they have read.
For these reasons, it is crucial that any summaries, trend visualisations or other analyses can be verified by the human analysts. Users should be able to verify the data by drilling down, e.
Most of EMM's graphs are interactive and allow viewing the underlying data. It would be useful if system providers additionally offered confidence values regarding the accuracy of their analyses.
For EMM, most specialised applications on individual information extraction tools include such tool evaluation results and an error analysis e.
However, the tools can behave very differently depending on the text type and the language, making the availability of drill-down functionality indispensable.
End users should be careful with accuracy statistics given by system providers. Especially commercial vendors but not only are good at presenting their systems in a very positive light.
For instance, our experience has shown that, especially in the field of sentiment analysis opinion mining, tonality , high accuracy is difficult to achieve even when the statistical accuracy measurement Precision and Recall are high.
Overall Precision accuracy for the system's predictions may for instance indeed be high when considering predictions for positive, negative and neutral sentiment, but this might simply be because the majority class e.
Accuracy statistics may also have been produced on an easy-to-analyse dataset while the data at hand may be harder to analyse.
Sentiment, for instance, may be easier to detect on product review pages on vending sites such as Amazon than on the news because journalists tend to want to give the impression of neutrality.
Machine learning approaches to text analysis are particularly promising because computers are good at optimising evidence and because machine learning tools are cheap to produce, compared to man-made rules.
However, the danger is that the automatically learnt rules are applied to texts that are different from the training data as comparable data rarely exists.
Manually produced rules might be easier to tune and to adapt. Again, statistics on the performance of automatic tools should be considered with care.
Within EMM, machine learning is used to learn vocabulary and recognition patterns, but these are then usually manually verified and generalised e.
Zavarella et al. To summarise: we firmly believe that Automated Content Analysis works when it is used with care and when its strengths and limits are known.
In: J. Kohlhammer, D. Keim eds. Golsar Germany : The Eurographics Association. In: U. Kock Wiil ed. Counterterrorism and Open Source Intelligence.
Computational Linguistics Applications, pp. Springer-Verlag, Berlin, Huttunen, A. Vihavainen, Roman Yangarber News Mining for Border Security Intelligence.
Detecting event-related links and sentiments from social media texts. Opinion Mining on Newspaper Quotations. Milano, Italy, Sentiment Analysis in the News.
Valletta, Malta, May PLoS One. Epub Mar 5. Transactions on Computational Collective Intelligence. Krstajic, M. Processing online news streams for large-scale semantic analysis.
EuroSurveillance Vol. Stockholm, 2 April Linge, J. Fuart, F. In: Malaga. Kostkova, M. Szomszor, and D. Fowler eds. Exploring the usefulness of cross-lingual information fusion for refining real-time news event extraction.
Proceedings of the social networks and application tools workshop SocNet pp. Skalica, Slovakia, September Geocoding multilingual texts: Recognition, Disambiguation and Visualisation.
Genoa, Italy, May Automatic Detection of Quotations in Multilingual News. Borovets, Bulgaria, Story tracking: linking similar news over time and across languages.
Manchester, UK, 23 August Building and displaying name relations using automatic unsupervised analysis of newspaper articles. Multilingual multi-document continuously updated social networks.
Borovets, Bulgaria, 26 September Sean P. O'Brien Anticipating the Good, the Bad, and the Ugly. Journal of Conflict Resolution, Vol.
Cross-lingual Named Entity Recognition. John Benjamins Publishing Company. ISBN 3. Steinberger Ralf A survey of methods to ease the development of highly multilingual Text Mining applications.
Boston, USA. Text Mining from the Web for Medical Intelligence. Weakly supervised approaches for ontology population.
Frontiers in Artificial Intelligence and Applications, Volume Semi-automatic acquisition of lexical resources and grammars for event extraction in Bulgarian and Czech.
Tanev Hristo Annals of Information Systems, Volume Sugumaran, M. Spiliopoulou, E. Enhancing Event Descriptions through Twitter Mining.
Dublin, June Available at:. Combining twitter and media reports on public health events in MedISys. Proceedings of the 22nd international conference on World Wide Web companion, pp.
Steinberger jrc. Werde die beste Version von Dir selbst. Jederzeit an jedem Ort! Lolathecur's Blog Below are two very important entries from the "Jewish Encyclopedia".
Jerome's Bible-Revision Work. Jerome's Bible-Translation Work. Jerome's Translation in Later Times. Earlier Latin Translations.
It was the product of the work of Jerome, one of the most learned and scholarly of the Church leaders of the early Christian centuries.
The earliest Latin version of the Scriptures seems to have originated not in Rome, but in one of Rome's provinces in North Africa.
Indeed, Tertullian c. There were at least two early Latin translations, one called the African and the other the European. These, based not on the Hebrew, but on the Greek, are thought to have been made before the text-work of such scholars as Origen, Lucian, and Hesychius, and hence would be valuable for the discovery of the Greek text with which Origen worked.
But the remains of these early versions are scanty. Jerome did not translate or revise several books found in the Latin Bible, and consequently the Old Latin versions were put in their places in the later Latin Bible.
The Psalter also exists in a revised form, and the books of Job and Esther, of the Old Latin, are found in some ancient manuscripts.
Only three other fragmentary manuscripts of the Old Testament in Old Latin are now known to be in existence. Jerome was born of Christian parents about , at Stridon, in the province of Dalmatia.
He received a good education, and carried on his studies at Rome, being especially fascinated by Vergil, Terence, and Cicero. Rhetoric and Greek also claimed part of his attention.
At Trier in Gaul he took up theological studies for several years. In he traveled in the Orient. In a severe illness he was so impressed by a dream that he dropped secular studies.
But his time had not been lost. He turned his brilliant mind, trained in the best schools of the day, to sacred things.
Like Moses and Paul, he retired to a desert, that of Chalcis, near Antioch, where he spent almost five years in profound study of the Scriptures and of himself.
At this period he sealed a friendship with Pope Damasus, who later opened the door to him for the great work of his life. In Jerome was ordained presbyter at Antioch.
Thence he went to Constantinople, where he was inspired by the expositions of Gregory Nazianzen. In he reached Rome, where he lived about three years in close friendship with Damasus.
For a long time the Church had felt the need of a good, uniform Latin Bible. Pope Damasus at first asked his learned friend Jerome to prepare a revised Latin version of the New Testament.
In the Four Gospels appeared in a revised form, and at short intervals thereafter the Acts and the remaining books of the New Testament.
These latter were very slightly altered by Jerome. Soon afterward he revised the Old Latin Psalter simply by the use of the Septuagint.
The name given this revision was the "Roman Psalter," in distinction from the "Psalterium Vetus. In he settled at Bethlehem, assumed charge of a monastery, and prosecuted his studies with great zeal.
He secured a learned Jew to teach him Hebrew for still better work than that he had been doing. His revision work had not yet ceased, for his Book of Job appeared as the result of the same kind of study as had produced the "Gallican Psalter.
But Jerome soon recognized the poor and unsatisfactory state of the Greek texts that he was obliged to use. This turned his mind and thought to the original Hebrew.
Friends, too, urged him to translate certain books from the original text. As a resultant of long thought, and in answer to many requests, Jerome spent fifteen years, to , on a new translation of the Old Testament from the original Hebrew text.
He began with the books of Samuel and Kings, for which he wrote a remarkable preface, really an introduction to the entire Old Testament.
He next translated the Psalms, and then the Prophets and Job. In he prepared a translation of Esdras and Chronicles.
After an interval of two years, during which he passed through a severe illness, he took up his arduous labors, and produced translations of Proverbs, Ecclesiastes, and Song of Songs.
The Pentateuch followed next, and the last canonical books, Joshua, Judges, Ruth, and Esther, were completed by The remainder of the Apocryphal books he left without revision or translation, as they were not found in the Hebrew Bible.
Jerome happily has left prefaces to most of his translations, and these documents relate how he did his work and how some of the earlier books were received.
Evidently he was bitterly criticized by some of his former best friends. His replies show that he was supersensitive to criticism, and often hot-tempered and stormy.
His irritability and his sharp retorts to his critics rather retarded than aided the reception of his translation.
But the superiority of the translation gradually won the day for most of his work. The Council of Trent in authorized the Latin Bible, which was by that time a strange composite.
The Old Testament was Jerome's translation from the Hebrew, except the Psalter, which was his Gallican revision; of the Apocryphal books, Judith and Tobit were his translations, while the remainder were of the Old Latin version.
These translations and revisions of translations, and old original translations, constitute the Vulgate.
See also Jerome. See fuller bibliography in S. Berger's work, mentioned above. His Knowledge of Hebrew. Church father; next to Origen, who wrote in Greek, the most learned student of the Bible among the Latin ecclesiastical writers, and, previous to modern times, the only Christian scholar able to study the Hebrew Bible in the original.
The dates of his birth and death are not definitely known; but he is generally assumed to have lived from to Born in Stridon, Dalmatia, he went as a youth to Rome, where he attended a school of grammar and rhetoric.
He then traveled in Gaul and Italy, and in went to Antioch, where he became the pupil of Apollinaris of Laodicea, the representative of the exegetical school of Antioch; subsequently, however, Jerome did not accept the purely historical exegesis of this school, but adopted more nearly the typic-allegoric method of Origen.
From Antioch he went to Chalcis in the Syrian desert, where he led the strictly ascetic life of a hermit, in atonement for the sins of his youth.
Here also he began with great labor to study Hebrew, with the aid of a baptized Jew ib. On a second visit to Antioch Jerome was ordained a priest.
He then went to Constantinople, and thence to Rome, where he undertook literary work for Pope Damasus, beginning at the same time his own Biblical works c.
He finally settled at Bethlehem in Palestine c. This outline of Jerome's life indicates that he was a master of Latin and Greek learning, and by studying furthermore Syriac and Hebrew united in his person the culture of the East and of the West.
His Teachers. It was in Bethlehem that he devoted himself most seriously to Hebrew studies. Jerome was not satisfied to study with any one Jew, but applied to several, choosing always the most learned preface to Hosea: "diceremque.
With similar words Jerome is always attempting to inspire confidence in his exegesis; but they must not be taken too literally, as he was wont to boast of his scholarship.
Of only three of his teachers is anything definite known. He was occasionally unwilling to explain the text ib. Jerome was frequently not satisfied with his teacher's exegesis, and disputed with him; and he often says that he merely read the Scriptures with him comm.
Another teacher is called "Baranina," i. He acquainted Jerome with a mass of Hebrew traditions, some of which referred especially to his native place, Tiberias.
This teacher of Aramaic was very prominent among the Jews, and Jerome, who had great difficulty in learning Aramaic, was very well satisfied with his instruction prefaces to Tobit and Daniel.
Jerome continued to study with Jews during the forty years that he lived in Palestine comm. His enemies frequently took him to task for his intercourse with the Jews; but he answered: "How can loyalty to the Church be impaired merely because the reader is informed of the different ways in which a verse is interpreted by the Jews?
This sentence characterizes the Jewish exegesis of that time. Jerome's real intention in studying the Hebrew text is shown in the following sentence: "Why should I not be permitted,.
Then when the Christians dispute with them, they shall have no excuse" ib. Vallarsi, ii. Jerome's knowledge of Hebrew is considerable only when compared with that of the other Church Fathers and of the general Christian public of his time.
His knowledge was really very defective. Although he pretends to have complete command of Hebrew and proudly calls himself a "trilinguis" being conversant with Latin, Greek, and Hebrew , he did not, in spite of all his hard work, attain to the proficiency of his simple Jewish teachers.
But he did not commit those errors into which the Christians generally fell; as he himself says: "The Jews boast of their knowledge of the Law when they remember the several names which we generally pronounce in a corrupt way because they are barbaric and we do not know their etymology.
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