Recording of presentation introducing narrative analysis, outlining what it is, why it can be a useful approach, how to do it and where to find out more. Presentation given at methods@manchester seminar at University of Manchester on 4 March 2010.

What is Narrative Analysis

Projections of Power: Framing News, Public Opinion, and U.S. Foreign Policy

Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next, we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

Pre-trained Models for Natural Language Processing: A Survey

VAN DIJK, TEUN A., News as Discourse Hillsdale, N.J.: Lawrence Erlbaum, 1988. $29.95 cloth. This book attempts the development of a "new, interdisciplinary theory of news in the press" (p. vii). It represents a very ambitious and somewhat speculative effort to weave together a broad range of existing news research approaches into a coherent, heuristic framework. Van Dijk succeeds in providing a useful summary of the literature in news research. Especially valuable is his discussion of recent European research. However, the overall framework is still at an early stage of development. Its utility remains to be demonstrated by future research. For some time now, communication researchers have talked of passing paradigms and ferment in the field. With the decline of past paradigms, our discipline has been left with a hodgepodge of small-scale theories. This is particularly true in the area of news research where various narrative, discourse and information processing theories abound. Van Dijk's book may signal a new era, an era in which efforts will be made to integrate existing conceptual fragments into broader frameworks. His work may be seen as providing a model for others who seek to make sense of our current proliferation of theories. Van Dijk's approach is centered in the tradition of discourse analysis, which evolved out of an integration of literary analysis and linguistics. However, he has aggressively modified earlier forms of discourse analysis in an effort to incorporate insights into the structure and interpretation of discourse derived from cognitive psychology. He is not content to simply apply discourse analysis to the evaluation of news stories. He recognizes the utility of constructing an approach which also considers the production of news by media practitioners and the interpretation of news by audience members. It is these broader concerns which set van Dijk's approach apart from previous analyses of news content. A central concept in van Dijk's theory is the notion of story schemas, which are defined as implicit structures that underlie typical stories. These schemas permit the easy production of news and also facilitate its interpretation by news consumers. The schema concept is at once powerful and ambiguous. There is growing research evidence that demonstrates the utility of positing the existence of cognitive structures (schemas) in people's minds which are activated by content cues and guide interpretion of all forms of communication. The schema concept helps to explain why complex and seemingly ambiguous messages often are easily interpreted by audience members. It also can explain why the same message can be interpreted in highly discrepent ways. If messages contain conflicting cues that lead people to activate different schemas, or if people don't share a homogeneous set of schemas, then it is likely that many contrasting interpretations of story content will be developed. But despite growing consensus concerning the utility of schema as a concept, researchers remain quite divided over both its definition and the type of research that will lead to the most useful findings. …

News as Discourse

Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy with four perspectives. Next, we describe how to adapt the knowledge of PTMs to the downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record

The semantic distance task: Quantifying semantic distance with semantic network path length.

In recent years, distributional models (DMs) have shown great success in representing lexical semantics. In this work we show that the extent to which DMs represent semantic knowledge is highly dependent on the type of knowledge. We pose the task of predicting properties of concrete nouns in a supervised setting, and compare between learning taxonomic properties (e.g., animacy) and attributive properties (e.g., size, color). We employ four state-of-the-art DMs as sources of feature representation for this task, and show that they all yield poor results when tested on attributive properties, achieving no more than an average F-score of 0.37 in the binary property prediction task, compared to 0.73 on taxonomic properties. Our results suggest that the distributional hypothesis may not be equally applicable to all types of semantic information.

How Well Do Distributional Models Capture Different Types of Semantic Knowledge

In the current study, two pools of 250 essays, all written as a response to the same prompt, were rated by two groups of raters (14 or 15 raters per group), thereby providing an approximation to the essay’s true score. An automated essay scoring (AES) system was trained on the datasets and then scored the essays using a cross-validation scheme. By eliminating one, two, or three raters at a time, and by calculating an estimate of the true scores using the remaining raters, an independent criterion against which to judge the validity of the human raters and that of the AES system, as well as the interrater reliability was produced. The results of the study indicated that the automated scores correlate with human scores to the same degree as human raters correlate with each other. However, the findings regarding the validity of the ratings support a claim that the reliability and validity of AES diverge: although the AES scoring is, naturally, more consistent than the human ratings, it is less valid.

Validating human and automated scoring of essays against “True” scores

This paper addresses the task of automatically detecting narrative structures in raw texts. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to news articles, motivated by their growing social impact as well as their role in creating and shaping public opinion. 
We introduce CompRes -- the first dataset for narrative structure in news media. We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for news media, by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success); then, we used that scheme to annotate a set of 29 English news articles (containing 1,099 sentences) collected from news and partisan websites. We use the annotated dataset to train several supervised models to identify the different narrative elements, achieving an $F_1$ score of up to 0.7. We conclude by suggesting several promising directions for future work.

CompRes: A Dataset for Narrative Structure in News.

The run time complexity of state-of-the-art inference algorithms in graph-based dependency parsing is super-linear in the number of input words (n). Recently, pruning algorithms for these models have shown to cut a large portion of the graph edges, with minimal damage to the resulting parse trees. Solving the inference problem in run time complexity determined solely by the number of edges (m) is hence of obvious importance. 
We propose such an inference algorithm for first-order models, which encodes the problem as a minimum spanning tree (MST) problem in an undirected graph. This allows us to utilize state-of-the-art undirected MST algorithms whose run time is O(m) at expectation and with a very high probability. A directed parse tree is then inferred from the undirected MST and is subsequently improved with respect to the directed parsing model through local greedy updates, both steps running in O(n) time. In experiments with 18 languages, a variant of the first-order MSTParser (McDonald et al., 2005b) that employs our algorithm performs very similarly to the original parser that runs an O(n^2) directed MST inference.

/pdf/edge-linear-first-order-dependency-parsing-with-undirected-3d5a3zauwj.pdf

Effi Levi

Papers

The semantic distance task: Quantifying semantic distance with semantic network path length.

How Well Do Distributional Models Capture Different Types of Semantic Knowledge

Validating human and automated scoring of essays against “True” scores

CompRes: A Dataset for Narrative Structure in News.

Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference