The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated.We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus.

We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ''trace'' categories of the treebank.

The Proposition Bank: An Annotated Corpus of Semantic Roles

Thank you for downloading constructions a construction grammar approach to argument structure. As you may know, people have search numerous times for their favorite readings like this constructions a construction grammar approach to argument structure, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their computer.

Constructions A Construction Grammar Approach To Argument Structure

https://www.bmj.com/content/bmj/1/5125/836.2.full.pdf

Dictionary of psychology

The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.

/pdf/semeval-2019-task-5-multilingual-detection-of-hate-speech-43evbafeqx.pdf

SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter

This volume collects three decades of articles by distinguish linguist Joan Bybee. Her articles essentially argue for the importance of frequency of use as a factor in the analysis and explanation of language structure. Her work has been very influential for a broad range of researchers in linguistics, particularly in discourse analysis, corpus linguistics, phonology, phonetics, and historical linguistics.

/pdf/frequency-of-use-and-the-organization-of-language-oxford-26i93ptyqg.pdf

Frequency of Use and the Organization of Language, Oxford University Press, Oxford 2007, VIII + 365 p. (Witold Mańczak)

https://framenet2.icsi.berkeley.edu/docs/r1.5/book.pdf

FrameNet II: Extended theory and practice

We present the pilot edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. It comprises two tasks, a coarse-grained binary classification task and a fine-grained multi-class classification task. The shared task had 20 participants submitting 51 runs for the coarse-grained task and 25 runs for the fine-grained task. Since this is a pilot task, we describe the process of extracting the raw-data for the data collection and the annotation schema. We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at https://projects.cai. fbi.h-da.de/iggsa/

/pdf/overview-of-the-germeval-2018-shared-task-on-the-2csy272iuc.pdf

Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language

We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.

/pdf/detection-of-abusive-language-the-problem-of-biased-datasets-285ztbogcr.pdf

Detection of Abusive Language: the Problem of Biased Datasets

/pdf/framenet-theory-and-practice-umol62e5wx.pdf

FrameNet: Theory and Practice

We describe the SemEval-2010 shared task on "Linking Events and Their Participants in Discourse". This task is an extension to the classical semantic role labeling task. While semantic role labeling is traditionally viewed as a sentence-internal task, local semantic argument structures clearly interact with each other in a larger context, e.g., by sharing references to specific discourse entities or events. In the shared task we looked at one particular aspect of cross-sentence links between argument structures, namely linking locally uninstantiated roles to their co-referents in the wider discourse context (if such co-referents exist). This task is potentially beneficial for a number of NLP applications, such as information extraction, question answering or text summarization.

Josef Ruppenhofer

Papers

FrameNet II: Extended theory and practice

Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language

Detection of Abusive Language: the Problem of Biased Datasets

FrameNet: Theory and Practice

SemEval-2010 Task 10: Linking Events and Their Participants in Discourse