Clearly

How to Write Clearly: Rules and Exercises on English Composition by Edwin Abbott Abbott, is part of the HackerNoon Books Series. You can jump to any chapter in this book . PREFACE. here PREFACE. Almost every English boy can be taught to write clearly, so far at least as clearness depends upon the arrangement of words. Force, elegance, and variety of style are more difficult to teach, and far more difficult to learn; but clear writing can be reduced to rules. To teach the art of writing clearly is the main object of these Rules and Exercises. Ambiguity may arise, not only from bad arrangement, but also from other causes—from the misuse of single words, and from confused thought. These causes are not removable by definite rules, and therefore, though not neglected, are not prominently considered in this book. My object rather is to point out some few continually recurring causes of ambiguity, and to suggest definite remedies in each case. Speeches in Parliament, newspaper narratives and articles, and, above all, resolutions at public meetings, furnish abundant instances of obscurity arising from the monotonous neglect of some dozen simple rules. The art of writing forcibly is, of course, a valuable acquisition—almost as valuable as the art of writing clearly. But forcible expression is not, like clear expression, a mere question of mechanism and of the manipulation of words; it is a much higher power, and implies much more. Writing clearly does not imply thinking clearly. A man may think and reason as obscurely as Dogberry himself, but he may (though it is not probable that he will) be able to write clearly for all that. Writing clearly—so far as arrangement of words is concerned—is a mere matter of adverbs, conjunctions, prepositions, and auxiliary verbs, placed and repeated according to definite rules.[1] Even obscure or illogical thought can be clearly expressed; indeed, the transparent medium of clear writing is not least beneficial when it reveals the illogical nature of the meaning beneath it. On the other hand, if a man is to write forcibly, he must (to use a well-known illustration) describe Jerusalem as "sown with salt," not as "captured," and the Jews not as being "subdued" but as "almost exterminated" by Titus. But what does this imply? It implies knowledge, and very often a great deal of knowledge, and it implies also a vivid imagination. The writer must have eyes to see the vivid side of everything, as well as words to describe what he sees. Hence forcible writing, and of course tasteful writing also, is far less a matter of rules than is clear writing; and hence, though forcible writing is exemplified in the exercises, clear writing occupies most of the space devoted to the rules. Boys who are studying Latin and Greek stand in especial need of help to enable them to write a long English sentence clearly. The periods of Thucydides and Cicero are not easily rendered into our idiom without some knowledge of the links that connect an English sentence. There is scarcely any better training, rhetorical as well as logical, than the task of construing Thucydides into genuine English; but the flat, vague, long-winded Greek-English and Latin-English imposture that is often tolerated in our examinations and is allowed to pass current for genuine English, diminishes instead of increasing the power that our pupils should possess over their native language. By getting marks at school and college for construing good Greek and Latin into bad English, our pupils systematically unlearn what they may have been allowed to pick up from Milton and from Shakespeare. I must acknowledge very large obligations to Professor Bain's treatise on "English Composition and Rhetoric," and also to his English Grammar. I have not always been able to agree with Professor Bain as to matters of taste; but I find it difficult to express my admiration for the systematic thoroughness and suggestiveness of his book on Composition. In particular, Professor Bain's rule on the use of "that" and "which" (see Rule 8) deserves to be better known.[2] The ambiguity produced by the confusion between these two forms of the Relative is not a mere fiction of pedants; it is practically serious. Take, for instance, the following sentence, which appeared lately in one of our ablest weekly periodicals: "There are a good many Radical members in the House cannot forgive the Prime Minister for being a Christian." Twenty years hence, who is to say whether the meaning is " , i.e. members in the House," or "there are a good many Radical members of the House cannot &c."? Professor Bain, apparently admitting no exceptions to his useful rule, amends many sentences in a manner that seems to me intolerably harsh. Therefore, while laying due stress on the utility of the rule, I have endeavoured to point out and explain the exceptions. who and they all the Radical that The rules are stated as briefly as possible, and are intended not so much for use by themselves as for reference while the pupil is working at the exercises. Consequently, there is no attempt to prove the rules by accumulations of examples. The few examples that are given, are given not to prove, but to illustrate the rules. The exercises are intended to be written out and revised, as exercises usually are; but they may also be used for instruction. The books being shut, the pupils, with their written exercises before them, may be questioned as to the reasons for the several alterations they have made. Experienced teachers will not require any explanation of the arrangement or rather non-arrangement of the exercises. They have been purposely mixed together unclassified to prevent the pupil from relying upon anything but his own common sense and industry, to show him what is the fault in each case, and how it is to be amended. Besides references to the rules, notes are attached to each sentence, so that the exercises ought not to present any difficulty to a painstaking boy of twelve or thirteen, provided he has first been fairly trained in English grammar. vivâ voce The "Continuous Extracts" present rather more difficulty, and are intended for boys somewhat older than those for whom the Exercises are intended. The attempt to modernize, and clarify, so to speak, the style of Burnet, Clarendon, and Bishop Butler,[3] may appear ambitious, and perhaps requires some explanation. My object has, of course, not been to the style of these authors, but to show how their meaning might be expressed more clearly in modern English. The charm of the style is necessarily lost, but if the loss is recognized both by teacher and pupil, there is nothing, in my opinion, to counterbalance the obvious utility of such exercises. Professor Bain speaks to the same effect:[4] "For an English exercise, the matter should in some way or other be supplied, and the pupil disciplined in giving it expression. I know of no better method than to prescribe passages containing good matter, but in some respects imperfectly worded, to be amended according to the laws and the proprieties of style. Our older writers might be extensively, though not exclusively, drawn upon for this purpose." improve upon To some of the friends whose help has been already acknowledged in "English Lessons for English People," I am indebted for further help in revising these pages. I desire to express especial obligations to the Rev. J. H. Lupton, late Fellow of St. John's College, Cambridge, and Second Master of St. Paul's School, for copious and valuable suggestions; also to several of my colleagues at the City of London School, among whom I must mention in particular the Rev. A. R. Vardy, Fellow of Trinity College, Cambridge. * * * * * Before electrotyping the Fourth and Revised Edition, I wish to say one word as to the manner in which this book has been used by my highest class, as a collection of Rules for reference in their construing lessons. In construing, from Thucydides especially, I have found Rules 5, 30, 34, 36, 37, and 40_a_, of great use. The rules about Metaphor and Climax have also been useful in correcting faults of taste in their Latin and Greek compositions. I have hopes that, used in this way, this little book may be of service to the highest as well as to the middle classes of our schools. FOOTNOTES: [1] Punctuation is fully discussed in most English Grammars, and is therefore referred to in this book only so far as is necessary to point out the slovenly fault of trusting too much to punctuation, and too little to arrangement. [2] Before meeting with Professor Bain's rule, I had shown that the difference between the Relatives is generally observed the Shakespeare. See "Shakespearian Grammar," paragraph 259. [3] Sir Archibald Alison stands on a very different footing. The extracts from this author are intended to exhibit the dangers of verbosity and exaggeration. [4] "English Composition and Rhetoric," p. vii. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Edwin Abbott Abbott (2007). How to Write Clearly: Rules and Exercises on English Composition. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/22600/pg22600.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Almost every English boy can be taught to write clearly

How to Write Clearly: Rules and Exercises on English Composition by Edwin Abbott Abbott is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : How to Write Clearly: Rules and Exercises on English Composition Title : Edwin Abbott Abbott Author : September 14, 2007 [eBook #22600] Most recently updated: January 2, 2021 Release date : English Language TABLE OF LINKS PREFACE INDEX OF RULES. I. CLEARNESS AND FORCE. . WORDS ORDER OF WORDS IN A SENTENCE. II. BREVITY. CLEARNESS AND FORCE. WORDS. ORDER OF WORDS IN A SENTENCE. BREVITY. EXERCISES CONTINUOUS EXERCISES CLEARNESS. BURNET. BISHOP BUTLER. BREVITY. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Edwin Abbott Abbott (2007). How to Write Clearly: Rules and Exercises on English Composition. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/22600/pg22600.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

How to Write Clearly: Rules and Exercises on English Composition - Table of Links

Dependency injection (DI) is a wonderful thing. Simply add your dependency as a parameter to the constructor (most commonly) of your class, register it with you DI container, and away you go - the DI container will manage the rest. Some of the key benefits of DI are: greater testability, greater maintainability, and greater reusability. : { { ( dbContent = MyDbContext())
        {
            dbContext.Orders.Add(order);
            dbContent.SaveChanges(); Ok();
        }
    } { ( dbContext = MyDbContext())
        { orders = dbContext.Orders.ToList(); JsonResult(orders);
        }
    }
} : { MyDbContext _dbContext; {
        _dbContext = dbContext;
    } {
        _dbContext.Orders.Add(order);
        _dbContent.SaveChanges(); Ok();
    } { orders = _dbContext.Orders.ToList(); JsonResult(orders);
    }
} // without DI public class OrderController Controller ActionResult ( ) public Post Order order using var new return ActionResult ( ) public Get using var new var return new // with DI public class OrderController Controller private readonly ( ) public OrderController MyDbContext dbContext ActionResult ( ) public Post Order order return ActionResult ( ) public Get var return new However, I recently came across a use case where DI can be a real pain - dependency injection in inherited classes. The Problem I have recently been working a lot with the package, using it's request/request handler pattern to issue commands in the system (inspired by ). Mediatr Jason Taylor's Clean Architecture solution I typically create a RequestHandler base class that contains common dependencies and functionality. Each concrete request handler can then inherit from this base class. < , > : < , > : < >
{ {
        DbContext = dbContext;
    } IApplicationDbContext DbContext { ; } ;
} MyRequestHandler : RequestHandler<MyRequest, MyResponse>
{ { } { }
} public abstract class RequestHandler TRequest TResponse IRequestHandler TRequest TResponse where TRequest IRequest TResponse ( ) public RequestHandler IApplicationDbContext dbContext protected get Task<TResponse> ( ) public abstract Handle TRequest request, CancellationToken cancellationToken public ( ) : ( ) public MyRequestHandler IApplicationDbContext dbContext base dbContext Task<MyResponse> ( ) public override Handle MyRequest request, CancellationToken cancellationToken // handler logic The problem comes when I want to add more dependencies to the base class. Now I have to go through to every single concrete request handler and update the constructor to take the new dependency as well. Fortunately, the code will not compile if I miss one, so there is no risk of a runtime error, but it is still incredibly tedious work to have to update every single request handler. Also, you can end up with very large constructors, which obscures the intention of the class. < , > : < , > : < >
{ {
        DbContext = dbContext;
        CurrentUser = currentUser;
    } IApplicationDbContext DbContext { ; } ICurrentUser CurrentUser { ; } ;
} MyRequestHandler : RequestHandler<MyRequest, MyResponse>
{ { } { }
} public abstract class RequestHandler TRequest TResponse IRequestHandler TRequest TResponse where TRequest IRequest TResponse ( ) public RequestHandler IApplicationDbContext dbContext, ICurrentUser currentUser protected get protected get Task<TResponse> ( ) public abstract Handle TRequest request, CancellationToken cancellationToken public ( ) : ( ) public MyRequestHandler IApplicationDbContext dbContext, ICurrentUser currentUser base dbContext, currentUser Task<MyResponse> ( ) public override Handle MyRequest request, CancellationToken cancellationToken // handler logic The Solution - Dependency Aggregates The solution to this problem is really quite simple. Rather than injecting the dependencies directly, create a new class that contains the dependencies (known as an aggregate) and inject that instead. {
    IApplicationDbContext DbContext { ; }
    ICurrentUser { ; }
} : { {
        DbContext = dbContext;
        CurrentUser = currentUser;
    } IApplicationDbContext DbContext { ; } ICurrentUser CurrentUser { ; }
} < , > : < , > : < >
{ {
        DbContext = aggregate.DbContext;
        CurrentUser = aggregate.CurrentUser;
    } IApplicationDbContext DbContext { ; } ICurrentUser CurrentUser { ; } ;
} MyRequestHandler : RequestHandler<MyRequest, MyResponse>
{ { } { }
} public interface IDependencyAggregate get get public class DependencyAggregate IDependencyAggregate ( ) public DependencyAggregate IApplicationDbContext dbContext, ICurrentUser currentUser public get public get public abstract class RequestHandler TRequest TResponse IRequestHandler TRequest TResponse where TRequest IRequest TResponse ( ) public RequestHandler IDependencyAggregate aggregate protected get protected get Task<TResponse> ( ) public abstract Handle TRequest request, CancellationToken cancellationToken public ( ) : ( ) public MyRequestHandler IDependencyAggregate aggregate base aggregate Task<MyResponse> ( ) public override Handle MyRequest request, CancellationToken cancellationToken // handler logic Now if I want to add a new dependency, the only places that I need to change the code are in the DependencyAggregate class and the RequestHandler base class (I don't need to make any changes to the inherited classes). Conclusion In this post I have described a simple method for managing dependency injection in inherited classes, by creating a dependency aggregate class to inject into the base class. This ensures that new dependencies can easily be introduced with having to make changes to every inherited class. I post mostly about full stack .NET and Vue web development. To make sure that you don't miss out on any posts, please follow this blog and . If you found this post helpful, please like it and share it. You can also find me on . subscribe to my newsletter Twitter Previously published at https://samwalpole.com/handling-dependency-injection-in-inherited-classes

How to Create a Dependency Aggregate Class to Manage DI: Clearly Exemplified

I lead the technology industry business unit for a fast-growing analytics firm. And one of the perks of my job is the numerous conversations I get to have with a variety of prospects or clients. I recently read the novel ‘ by Rolf Dobelli, which deals with systematic cognitive errors of human beings as a result of our evolution. The Art of Thinking Clearly’ I enjoyed reading the book immensely, relating those cognitive errors to what I experience every day. I am selecting a few rampant cognitive errors mentioned in the book and linking them to situations I have come across. Neo-mania Def: The mania for all things new and shiny. The desire to have automation, machine models, artificial intelligence, and other such cutting-edge capabilities is intense in organizations today and probably justified. However, it’s alarming to see many of these efforts promise a very large ROI but never deliver it. marketing learning I once had an executive of a large organization say without batting an eyelid - “We have invested millions in a big data environment, and we now need to figure how to use it.” I am always disappointed when I hear things like — “Can you tell me how I can use machine learning capabilities in my business/function?” I wonder if anyone would say it if I replaced ‘machine learning’ with ‘statistics’. Conversely, the best use of ML/AI is when the need or a business problem was identified first and ML/AI emerged as a solution. Swimmer’s Body Illusion Def: Confusing the process of selection for effect. Rolf explains in the book that swimmers don’t have great bodies because they swim a lot; they are swimmers because their physique is suitable for swimming. I have seen companies falling to this illusion when they look to replicate an analytics success story from companies that seem to be doing very well and claiming that analytics is driving their success. It’s safe to say that the retail industry has the most mature analytics capability, but nobody is blaming the analytics team at Sears and Macy’s for their decline. In my opinion, most companies would succeed in their analytics initiatives if they just are more rational and use data to be just that. (Read more: Building an analytics-first organization ) Authority Bias Def: The tendency of human beings to follow authority without question, a culture most visible in religion but equally prominent in businesses. When I ask my clients why they were following a particular strategy, the responses most often indicate an authority bias. Interestingly, the bias seems more obvious when the perceived results from the strategy are less obvious. My advice to my clients has always been to focus on outcomes and not on the process. I am amazed even now that such a conversation isn’t always easy. The Paradox of Choice Def: Too many choices causing decision paralysis and subsequently leading to poor decisions. The number of , vendors, and technologies today is confounding. One would expect then that most clients would be happy with the choices they make. However, I haven’t seen a company that is entirely happy with its selection of business intelligence (BI) or . analytics solutions analytics platforms If only they hadn’t compared the tools with each other but against a prioritized list of their own needs, they would have come to a better decision. Default Effect/Status Quo Bias Def: A strong tendency to cling to the way things are, even when they put us at a disadvantage. Most tech companies with revenues of $500 million to $2 billion are hyper-focused on growth. The most important part of achieving growth is to size the market right, find the account segments to focus on, and then have the sales force or channel partners gain market share. One would imagine that these companies would have nailed this process well. I am yet to come across one. (Read more: Business Transformation — How Big Data Analytics Helps ) Alternative Blindness Def: Systematically forgetting to compare an existing offer with the next-best alternative. I have run across companies with no analytics capabilities looking to hire a team first, spending a million dollars, and losing quite a bit of time before they see any capability in action. When we provide them the alternative to spend less than half a million and have the capabilities stand up in six months, one would imagine it would be a no-brainer. It is, but only to our clients. *Please note that the terms and their descriptions are from Rolf Dobelli’s book. (This article was authored by Maruti Peri and first appeared on LinkedIn , the BRIDGEi2i blog & Datafloq .)

Master “The Art of Thinking Clearly” Before Making Your Analytics Investments!

Next-Gen FHE Toolchains for the Dev Multiverse: How TFHE Is Taking Us There

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro
Diverse cross-document coreference and media bias analysis
Annotation tool
Annotation guidelines
Conclusion and future work
Acknowledgements
Bibliographical References 2. Diverse cross-document coreference and media bias analysis Media bias is a multifaceted phenomenon of news coverage that is one-sided, politically shaded, or in some other way non-neutral. It can occur in all sorts of news media, though we focus on digital print media, only. One specific type of media bias is bias by word-choice and labeling (Hamborg et al., 2019). Word choice describes the selection from a variety of possible expressions to refer to an entity. For example, in order to refer to the USA’s current head of state, journalists could use one of the relatively neutral alternatives “Joe Biden”, “Biden”, or “the US president”, or in theory, choose a clearly biased expression like ”the dictator” (Kurmelovs, 2023). Labeling, on the other hand, describes the assignment of attributes to an expression, inter alia by adding adjectives. Examples for bias by labeling include “an anxious and uncertain president” or “crooked Joe Biden” (Luciano, 2023). Together, word-choice and labelling form a so-called frame (Hamborg et al., 2019). In news articles, frames are used in a variety of ways, either for the sake of linguistic diversity or to make certain, potentially biased statements about an entity. To test an article for such statements, all of an entity’s frames need to be extracted and evaluated together. Hence, before an article can be properly analyzed with regards to if and how it uses biased frames of (certain) entities, we are first faced with the task of identifying such frames. The identification of all expressions that refer to the same entity is a matter of coreference resolution. To conclude, successful coreference resolution is a prerequisite to any further inquiry of media bias by word-choice and labelling. As already indicated above, automatic coreference resolution does show good results in extracting identity clusters from a document (Liu et al., 2023). However, we have seen that there exist nearidentity relations between expressions, potentially even across documents, that would be mostly overseen by standard coreference resolution approaches (Zhukova et al., 2022). Hence, they would also be overseen by any media bias analysis that depends on coreference resolution. We hope that our building of a corpus for diverse cross-document coreference will contribute to the analysis of media bias by providing data that contains the full variety of frames used in news articles. Eventually, we would like to test how we can measure media bias by focusing on diverse coreference in news articles. To answer this last question, though, an additional layer of media bias annotation would have to be put upon our coreference data (Spinde et al., 2021a,b). This paper is available on arxiv under CC 4.0 license. Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro Diverse cross-document coreference and media bias analysis Annotation tool Annotation guidelines Conclusion and future work Acknowledgements Bibliographical References Abstract and Intro Abstract and Intro Diverse cross-document coreference and media bias analysis Diverse cross-document coreference and media bias analysis Annotation tool Annotation tool Annotation guidelines Annotation guidelines Conclusion and future work Conclusion and future work Acknowledgements Acknowledgements Bibliographical References Bibliographical References 2. Diverse cross-document coreference and media bias analysis Media bias is a multifaceted phenomenon of news coverage that is one-sided, politically shaded, or in some other way non-neutral. It can occur in all sorts of news media, though we focus on digital print media, only. One specific type of media bias is bias by word-choice and labeling (Hamborg et al., 2019). Word choice describes the selection from a variety of possible expressions to refer to an entity. For example, in order to refer to the USA’s current head of state, journalists could use one of the relatively neutral alternatives “Joe Biden”, “Biden”, or “the US president”, or in theory, choose a clearly biased expression like ”the dictator” (Kurmelovs, 2023). bias by word-choice and labeling Labeling, on the other hand, describes the assignment of attributes to an expression, inter alia by adding adjectives. Examples for bias by labeling include “an anxious and uncertain president” or “crooked Joe Biden” (Luciano, 2023). Together, word-choice and labelling form a so-called frame (Hamborg et al., 2019). In news articles, frames are used in a variety of ways, either for the sake of linguistic diversity or to make certain, potentially biased statements about an entity. To test an article for such statements, all of an entity’s frames need to be extracted and evaluated together. Hence, before an article can be properly analyzed with regards to if and how it uses biased frames of (certain) entities, we are first faced with the task of identifying such frames. The identification of all expressions that refer to the same entity is a matter of coreference resolution. To conclude, successful coreference resolution is a prerequisite to any further inquiry of media bias by word-choice and labelling. frame As already indicated above, automatic coreference resolution does show good results in extracting identity clusters from a document (Liu et al., 2023). However, we have seen that there exist nearidentity relations between expressions, potentially even across documents, that would be mostly overseen by standard coreference resolution approaches (Zhukova et al., 2022). Hence, they would also be overseen by any media bias analysis that depends on coreference resolution. We hope that our building of a corpus for diverse cross-document coreference will contribute to the analysis of media bias by providing data that contains the full variety of frames used in news articles. Eventually, we would like to test how we can measure media bias by focusing on diverse coreference in news articles. To answer this last question, though, an additional layer of media bias annotation would have to be put upon our coreference data (Spinde et al., 2021a,b).

Code Book for Annotation of Diverse Cross-Document Coreference: Bibliographical References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro
Diverse cross-document coreference and media bias analysis
Annotation tool
Annotation guidelines
Conclusion and future work
Acknowledgements
Bibliographical References Abstract This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wikidata’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling. Keywords: coreference resolution, diverse coreference annotation, entity annotation, entity linking, media bias analysis, natural language processing 1. Introduction Coreference is the phenomenon of several expressions in a text all referring to the same person, object, or other entity or event as their referent. Thus, in a narrow sense, analyzing a document with regards to coreference means detecting relations of identity between phrases. The following example (1) illustrates such an identity relation, where coreferential expressions are printed in italics: (1) “Joe Biden arrived in Berlin yesterday, but the president did not come alone.” In (1), the noun phrase “Joe Biden” introduces a new entity while “the president” relates back to that introducing phrase. Within this relation, the introducing phrase “Joe Biden” is called the antecedent while the back-relating phrase “the president” is called an anaphor. Both expressions are coreferential in the way that they refer to the same non-textual entity, namely to the actual ‘realworld’ Joe Biden or at least to a corresponding mental concept. We can think of an antecedent and its anaphora as forming a cluster of mentions that as a whole represents its extra-textual referent within a textual document, as shown in Figure 1. As a task of natural language processing (NLP), coreference resolution has become quite efficient in detecting identity relations between phrases. However, reflecting on how we use language to refer to something, we are forced to realize that coreference in a broader sense is actually far more complex. We can address an entity or event by using a variety of expressions that are in fact not strictly identical to each other. Consider the following examples: (2) “President Biden was clearly not satisfied with today’s outcome. As the White House stated this afternoon, efforts will be made to . . . ” (3) “Even if the young Erdogan used to be pro-Western, Turkey’s president nowadays often acts against Western interests.” (4) “The AfD is circulating a photo of Angela Merkel with a Hijab, although Merkel never wore Muslim clothes.” In these given examples, the highlighted mentions mean ‘almost’ the same, but not completely. In (2), we are aware by world-knowledge that ”the White House” is often used as a substitute expression for the current US president, although the former is a place which in strict terms cannot be identical to the president, who is a person. In (3), on the other hand, both mentions refer to the ’real-world’ person Erdogan, but at different time steps. Finally, in (4), a mention representing the person Merkel is juxtaposed with a mention representing a picture of Merkel. While these two mentions could refer to separate entities, the juxtaposition indicates a connection between both where the attributes of the first mention do influence the perception of the second mention. Hence, we would miss essential semantic connections if we chose not to mark them as coreferential. Having said that, the simple classification of two mentions into coreferential (identical) or noncoreferential (non-identical) does not seem to suffice the complexity of common text data. Instead, we need to allow for diverse coreference clusters that include finer-grained relations lying between identity and non-identity. We need to allow for near-identity relations to mark two mentions that are partially, but not totally, identical (Recasens et al., 2010). In news coverage, identity and near-identity references are extensively used to report on persons, organizations, and other entities of public interest. It is our goal to build up a corpus that contains annotated examples of such diverse forms of coreference. While diverse coreference occurs in all sorts of news media, we focus on digital print media, only. Furthermore, although in practice both entities and events can act as referent, we ignore references to events for now, as their annotation would go beyond the limits of our present scheme.[1] The ordinary business of journalism is to write about current political affairs and other happenings of public interest. These happenings are normally reported by several newspapers at the same time. All of these news articles are considered documents that contain references to the same entities and together form a discourse about them. To include the whole picture of such intradiscursive references, we want our corpus to link document-level clusters with corresponding clusters of other documents of the same discourse. Hence, our corpus is to depict cross-document coreference data. On a discourse level, corresponding clusters form discourse entities that themselves can be linked to their non-textual referents by some knowledge graph identifier. For this project, we use Wikidata’s Uniform Resource Identifiers (URIs) for entity linking. By doing so, world knowledge is included into the data. This allows for drawing connections even between different discourse entities that refer to a common referent, yet at a different time step or rather in the context of a different happening. Figure 2 illustrates the multiple layers of this annotation model. In building a corpus for diverse cross-document coreference in news articles, we hope to provide a valuable resource for the evaluation of automated coreference resolution tasks. The contribution of this paper mainly lies in providing an answer to the question of how to create such a corpus. How can diverse coreference relations be annotated in a cross-document setup? We believe our scheme as we present it here tackles this problem efficiently, extensively, and unambiguously. Additionally, we would like to use the data resulting from our own annotations for further research in the area of media bias. Even if it plays no direct part in the outlined scheme, a lot of our choices how to annotate references were made because of this requirement to make the data usable for later media bias analysis. Eventually, we hope to contribute to the wider research question of how to identify media bias by word choice and labelling based on the usage of diverse coreference relations in news articles. The following section 2 will further elaborate on this connection between diverse coreference and the problem of media bias analysis. Despite its only subtle impact on our practical annotation instructions, that section means to highlight the theoretical background and motivation behind our project. The sections thereafter will then deal with the actual annotation process. Section 3 will guide coders through the setup and controls of Inception, our selected annotation software. Finally, section 4 will define annotation instructions in three passes while also outlining our typology of diverse coreference. The data we use for our own annotations consists of the text bodies of articles that report on the same happenings. All articles are in English and were published by one of the following US-American newspapers: HuffPost (categorized as ”Left” by AllSides (2023) or ”Skews Left” by Ad Fontes Media (2023), abbreviated in our data as ”LL”), The New York Times (categorized as ”Lean Left” by AllSides or ”Skews Left” by Ad Fontes Media, abbreviated as ”L”), USA Today (categorized as ”Lean Left” or ”Middle or Balanced Bias”, abbreviated as ”M”), Fox News (categorized as ”Right” or ”Skews Right”, abbreviated as ”R”), Breitbart News Network (categoized as ”Right” or ”Strong Right”, abbreviated as ”RR”).[2] [1] Though at a later point, this scheme could be extended to also include the annotation of events (Linguistic Data Consortium, 2005; O’Gorman et al., 2016). [2] Looking at the political orientation of these newspapers, the data is unbalanced with an underrepresentation of politically centered media. However, at the current state of this project, the imbalance is unlikely to influence our analysis which does not yet target political orientation or media bias itself. Therefore, we will ignore this issue for now. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro Diverse cross-document coreference and media bias analysis Annotation tool Annotation guidelines Conclusion and future work Acknowledgements Bibliographical References Abstract and Intro Abstract and Intro Diverse cross-document coreference and media bias analysis Diverse cross-document coreference and media bias analysis Annotation tool Annotation tool Annotation guidelines Annotation guidelines Conclusion and future work Conclusion and future work Acknowledgements Acknowledgements Bibliographical References Bibliographical References Abstract This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wikidata’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling. Keywords : coreference resolution, diverse coreference annotation, entity annotation, entity linking, media bias analysis, natural language processing Keywords 1. Introduction Coreference is the phenomenon of several expressions in a text all referring to the same person, object, or other entity or event as their referent. Thus, in a narrow sense, analyzing a document with regards to coreference means detecting relations of identity between phrases. The following example (1) illustrates such an identity relation, where coreferential expressions are printed in italics: (1) “Joe Biden arrived in Berlin yesterday, but the president did not come alone.” In (1), the noun phrase “Joe Biden” introduces a new entity while “the president” relates back to that introducing phrase. Within this relation, the introducing phrase “Joe Biden” is called the antecedent while the back-relating phrase “the president” is called an anaphor. Both expressions are coreferential in the way that they refer to the same non-textual entity, namely to the actual ‘realworld’ Joe Biden or at least to a corresponding mental concept. We can think of an antecedent and its anaphora as forming a cluster of mentions that as a whole represents its extra-textual referent within a textual document, as shown in Figure 1. As a task of natural language processing (NLP), coreference resolution has become quite efficient in detecting identity relations between phrases. However, reflecting on how we use language to refer to something, we are forced to realize that coreference in a broader sense is actually far more complex. We can address an entity or event by using a variety of expressions that are in fact not strictly identical to each other. Consider the following examples: (2) “ President Biden was clearly not satisfied with today’s outcome. As the White House stated this afternoon, efforts will be made to . . . ” President Biden the White House (3) “Even if the young Erdogan used to be pro-Western, Turkey’s president nowadays often acts against Western interests.” the young Erdogan Turkey’s president (4) “The AfD is circulating a photo of Angela Merkel with a Hijab , although Merkel never wore Muslim clothes.” a photo of Angela Merkel with a Hijab In these given examples, the highlighted mentions mean ‘almost’ the same, but not completely. In (2), we are aware by world-knowledge that ” the White House ” is often used as a substitute expression for the current US president, although the former is a place which in strict terms cannot be identical to the president, who is a person. In (3), on the other hand, both mentions refer to the ’real-world’ person Erdogan, but at different time steps. Finally, in (4), a mention representing the person Merkel is juxtaposed with a mention representing a picture of Merkel. While these two mentions could refer to separate entities, the juxtaposition indicates a connection between both where the attributes of the first mention do influence the perception of the second mention. Hence, we would miss essential semantic connections if we chose not to mark them as coreferential. Having said that, the simple classification of two mentions into coreferential (identical) or noncoreferential (non-identical) does not seem to suffice the complexity of common text data. Instead, we need to allow for diverse coreference clusters that include finer-grained relations lying between identity and non-identity. We need to allow for near-identity relations to mark two mentions that are partially, but not totally, identical (Recasens et al., 2010). the White House diverse coreference near-identity In news coverage, identity and near-identity references are extensively used to report on persons, organizations, and other entities of public interest. It is our goal to build up a corpus that contains annotated examples of such diverse forms of coreference. While diverse coreference occurs in all sorts of news media, we focus on digital print media, only. Furthermore, although in practice both entities and events can act as referent, we ignore references to events for now, as their annotation would go beyond the limits of our present scheme.[1] The ordinary business of journalism is to write about current political affairs and other happenings of public interest. These happenings are normally reported by several newspapers at the same time. All of these news articles are considered documents that contain references to the same entities and together form a discourse about them. To include the whole picture of such intradiscursive references, we want our corpus to link document-level clusters with corresponding clusters of other documents of the same discourse. Hence, our corpus is to depict cross-document coreference data. On a discourse level, corresponding clusters form discourse entities that themselves can be linked to their non-textual referents by some knowledge graph identifier. For this project, we use Wikidata’s Uniform Resource Identifiers (URIs) for entity linking. By doing so, world knowledge is included into the data. This allows for drawing connections even between different discourse entities that refer to a common referent, yet at a different time step or rather in the context of a different happening. Figure 2 illustrates the multiple layers of this annotation model. In building a corpus for diverse cross-document coreference in news articles, we hope to provide a valuable resource for the evaluation of automated coreference resolution tasks. The contribution of this paper mainly lies in providing an answer to the question of how to create such a corpus. How can diverse coreference relations be annotated in a cross-document setup? We believe our scheme as we present it here tackles this problem efficiently, extensively, and unambiguously. Additionally, we would like to use the data resulting from our own annotations for further research in the area of media bias. Even if it plays no direct part in the outlined scheme, a lot of our choices how to annotate references were made because of this requirement to make the data usable for later media bias analysis. Eventually, we hope to contribute to the wider research question of how to identify media bias by word choice and labelling based on the usage of diverse coreference relations in news articles. The following section 2 will further elaborate on this connection between diverse coreference and the problem of media bias analysis. Despite its only subtle impact on our practical annotation instructions, that section means to highlight the theoretical background and motivation behind our project. The sections thereafter will then deal with the actual annotation process. Section 3 will guide coders through the setup and controls of Inception, our selected annotation software. Finally, section 4 will define annotation instructions in three passes while also outlining our typology of diverse coreference. The data we use for our own annotations consists of the text bodies of articles that report on the same happenings. All articles are in English and were published by one of the following US-American newspapers: HuffPost (categorized as ”Left” by AllSides (2023) or ”Skews Left” by Ad Fontes Media (2023), abbreviated in our data as ”LL”), The New York Times (categorized as ”Lean Left” by AllSides or ”Skews Left” by Ad Fontes Media, abbreviated as ”L”), USA Today (categorized as ”Lean Left” or ”Middle or Balanced Bias”, abbreviated as ”M”), Fox News (categorized as ”Right” or ”Skews Right”, abbreviated as ”R”), Breitbart News Network (categoized as ”Right” or ”Strong Right”, abbreviated as ”RR”).[2] [1] Though at a later point, this scheme could be extended to also include the annotation of events (Linguistic Data Consortium, 2005; O’Gorman et al., 2016). [2] Looking at the political orientation of these newspapers, the data is unbalanced with an underrepresentation of politically centered media. However, at the current state of this project, the imbalance is unlikely to influence our analysis which does not yet target political orientation or media bias itself. Therefore, we will ignore this issue for now.

CLEARLY

clearly.earth

1-10 employees

Since 2021

COMPANY RANKING

CLEARLY

EVERGREEN INDEX #3208

Representing this company? Click here to claim and customize this page!

Clearly WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

CLEARLY

clearly.earth

1-10 employees

Since 2021

COMPANY RANKING

CLEARLY

EVERGREEN INDEX #3208

RELATED COMPANIESRANK

STORIES ABOUT CLEARLY

Representing this company? Click here to claim and customize this page!

Clearly WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!