Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Not by Genes Alone: How Culture Transformed Human Evolution

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Qu'est-ce qu'un auteur ?

Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated. While there is some agreement regarding automatic metrics, there is a high degree of variation in the way that human evaluation is carried out. This paper provides an overview of how human evaluation is currently conducted, and presents a set of best practices, grounded in the literature. With this paper, we hope to contribute to the quality and consistency of human evaluations in NLG.

/pdf/best-practices-for-the-human-evaluation-of-automatically-2obzwsceqx.pdf

Best practices for the human evaluation of automatically generated text

We shed new light on the authenticity of the writings of Julius Caesar.Hirtius, one of Caesar's generals, must have contributed to Caesar's writings.We benchmark two authorship verification systems on publicly available data sets.We test on both modern data sets, and Latin texts from Antiquity.We show how computational methods inform traditional authentication studies. In this paper, we shed new light on the authenticity of the Corpus Caesarianum, a group of five commentaries describing the campaigns of Julius Caesar (100-44 BC), the founder of the Roman empire. While Caesar himself has authored at least part of these commentaries, the authorship of the rest of the texts remains a puzzle that has persisted for nineteen centuries. In particular, the role of Caesar's general Aulus Hirtius, who has claimed a role in shaping the corpus, has remained in contention. Determining the authorship of documents is an increasingly important authentication problem in information and computer science, with valuable applications, ranging from the domain of art history to counter-terrorism research. We describe two state-of-the-art authorship verification systems and benchmark them on 6 present-day evaluation corpora, as well as a Latin benchmark dataset. Regarding Caesar's writings, our analyses allow us to establish that Hirtius's claims to part of the corpus must be considered legitimate. We thus demonstrate how computational methods constitute a valuable methodological complement to traditional, expert-based approaches to document authentication.

https://pure.knaw.nl/ws/files/2895651/caesar_author.pdf

Authenticating the writings of Julius Caesar

This paper describes a co-creative text generation system applied within a science fiction setting to be used by an established novelist. The project was initiated as part of The Dutch Book Week, and the generated text will be published within a volume of science fiction stories. We explore the ramifications of applying Natural Language Generation within a cocreative process, and examine where the cocreative setting challenges both writer and machine. We employ a character-level language model to generate text based on a large corpus of Dutch novels that exposes a number of tunable parameters to the user. The system is used through a custom graphical user interface, that helps the writer to elicit, modify and incorporate suggestions by the text generation system. Besides a literary work, the output of the present project also includes user-generated meta-data that is expected to contribute to the quantitative evaluation of the text-generation system and the co-creative process involved.

/pdf/synthetic-literature-writing-science-fiction-in-a-co-4x44ku7exr.pdf

Synthetic Literature: Writing Science Fiction in a Co-Creative Process

In this paper we report on an explorative study of the history of the twentieth cen- tury from a lexical point of view. As data, we use a diachronic collection of 270,000+ English-language articles har- vested from the electronic archive of the well-known Time Magazine (1923–2006). We attempt to automatically identify significant shifts in the vocabulary used in this corpus using efficient, yet unsupervised computational methods, such as Parsimonious Language Models. We offer a qualitative interpretation of the outcome of our experiments in the light of momen- tous events in the twentieth century, such as the Second World War or the rise of the Internet. This paper follows up on a recent string of frequentist approaches to studying cultural history (‘Culturomics’), in which the evolution of human culture is studied from a quantitative perspective, on the basis of lexical statistics extracted from large, textual data sets.

/pdf/mining-the-twentieth-centurya-s-history-from-the-time-2h57vz9m5r.pdf

Mining the Twentieth Centuryâ€™s History from the Time Magazine Corpus

This paper describes the development of the Dutch Folktale Database as a digital archive of intangible heritage and a sophisticated research instrument. Current research focuses on automating the assignment of metadata to folktales and on obtaining a better understanding of classifications of folktales into types, as well as an understanding of motif sequences. These studies are useful for future fundamental research into patterns and structures, such as historical and geographical variation in oral tradition, on an international scale.

/pdf/automatic-enrichment-and-classification-of-folktales-in-the-2lqe5ntz8u.pdf

Automatic Enrichment and Classification of Folktales in the Dutch Folktale Database

We present a system to extract ranked lists of actors from fairytales ordered by importance. This task requires more than a straightforward application of generic methods such as Named Entity Recognition. We show that by focusing on two specific linguistic constructions that reflect the intentionality of a subject, direct and indirect speech, we obtain a high-precision method to extract the cast of a story. The system we propose contains a new method based on the dispersion of terms to rank the different cast members on a scale of importance to the story. 1 Introducing the Problem Today do I bake, tomorrow I brew, The day after that the queen’s child comes in; And oh! I am glad that nobody knew That the name I am called is Rumpelstiltskin! So says the song of the little man in Brother Grimm’s Rumpelstiltskin with which he accidentally reveals his name to the queen. Advances in the task of Named Entity Recognition (NER) make it possible for computer systems to recognize his name as well. NER seeks to locate and extract atomic textual units into a predefined set of categories (e.g. PERSON, LOCATION and ORGANIZATION). Now that we can recognize Rumpelstiltskin as being the name of a person, how do we know that he is a character or actor in the story? Are all named entities within the category of PERSON in a story automatically actors? How about characters that have no proper name in a story, but are referred to by nominals or noun phrases? How do we know that they are part of the cast of a story? Most NER systems are developed for the domain of non-fiction, including newspapers, manuals and so forth. Applications of NER in literary texts or folktales, however, have been discussed only marginally (see [13] for an exception).

/pdf/casting-a-spell-identification-and-ranking-of-actors-in-35ywkboz6f.pdf

Folgert Karsdorp

Papers

Authenticating the writings of Julius Caesar

Synthetic Literature: Writing Science Fiction in a Co-Creative Process

Mining the Twentieth Centuryâ€™s History from the Time Magazine Corpus

Automatic Enrichment and Classification of Folktales in the Dutch Folktale Database

Casting a Spell: Identification and Ranking of Actors in Folktales