Sign language processing is the field of research that aims to recognize, retrieve, and spot signs in videos. Various approaches have been developed, varying in whether they use linguistic features and whether they use landmark detection tools or not. Incorporating linguistics holds promise for improving sign language processing in terms of performance, generalizability, and explainability. This paper focuses on the task of sign spotting and aims to expand on the approximative linguistic features that have been used in previous work, and to understand when linguistic features deliver an improvement over landmark features. We detect landmarks with Mediapipe and extract linguistically relevant features from them, including handshape, orientation, location, and movement. We compare a sign spotting model using linguistic features with a model operating on landmarks directly, finding that the approximate linguistic features tested in this paper capture some aspects of signs better than the landmark features, while they are worse for others.

We introduce a simple concept tracking approach to support conceptual history research. Building on the existing practices of conceptual historians, we use dictionaries to identify “anchors”, which represent primary dimensions of meaning of a concept. Then, we create a plot showing how a key concept has evolved over time in a historical corpus in relation to these dimensions. We demonstrate the approach by plotting the change of several key concepts in the COHA corpus.

Deep learning holds great promise for detecting discriminatory language in the public sphere. However, for the detection of illegal age discrimination in job advertisements, regex approaches are still strong performers. In this paper, we investigate job advertisements in the Netherlands. We present a qualitative analysis of the benefits of the ‘old’ approach based on regexes and investigate how neural embeddings could address its limitations.

This paper makes the case for studying concreteness in language as a bridge that will allow language technology to support the understanding and improvement of ethnic inclusivity in job advertisements. We propose an annotation scheme that guides the assignment of sentences in job ads to classes that reflect concrete actions, i.e., what the employer needs people to do, and abstract dispositions, i.e., who the employer expects people to be. Using an annotated dataset of Dutch-language job ads, we demonstrate that machine learning technology is effectively able to distinguish these classes.

We carry out a case study on the use of data programming to create data to train classifiers used for product moderation on a large e-commerce platform. Data programming is a recently-introduced technique that uses human-defined rules to generate training data sets without tedious item-by-item hand labeling. Our study investigates methods for allowing product moderators to quickly modify the rules given their knowledge of the domain and, especially, of textual item descriptions. Our results show promise that moderators can use this approach to steer the training data, making possible fast and close control of classifiers that detect policy violations.

This paper presents a typology of errors produced by automatic summarization systems. The typology was created by manually analyzing the output of four recent neural summarization systems. Our work is motivated by the growing awareness of the need for better summary evaluation methods that go beyond conventional overlap-based metrics. Our typology is structured into two dimensions. First, the Mapping Dimension describes surface-level errors and provides insight into word-sequence transformation issues. Second, the Meaning Dimension describes issues related to interpretation and provides insight into breakdowns in truth, i.e., factual faithfulness to the original text. Comparative analysis revealed that two neural summarization systems leveraging pre-trained models have an advantage in decreasing grammaticality errors, but not necessarily factual errors. We also discuss the importance of ensuring that summary length and abstractiveness do not interfere with evaluating summary quality.

We report on a case study of text and images that reveals the inadequacy of simplistic assumptions about their connection and interplay. The context of our work is a larger effort to create automatic systems that can extract event information from online news articles about flooding disasters. We carry out a manual analysis of 1000 articles containing a keyword related to flooding. The analysis reveals that the articles in our data set cluster into seven categories related to different topical aspects of flooding, and that the images accompanying the articles cluster into five categories related to the content they depict. The results demonstrate that flood-related news articles do not consistently report on a single, currently unfolding flooding event and we should also not assume that a flood-related image will directly relate to a flooding-event described in the corresponding article. In particular, spatiotemporal distance is important. We validate the manual analysis with an automatic classifier demonstrating the technical feasibility of multimedia analysis approaches that admit more realistic relationships between text and images. In sum, our case study confirms that closer attention to the connection between text and images has the potential to improve the collection of multimodal information from news articles.

We describe the development of a test collection for the investigation of speech retrieval beyond identification of relevant content. This collection focuses on satisfying user information needs for queries associated with specific types of speech acts. The collection is based on an archive of the Internet video from Internet video sharing platform (blip.tv), and was provided by the MediaEval benchmarking initiative. A crowdsourcing approach was used to identify segments in the video data which contain speech acts, to create a description of the video containing the act and to generate search queries designed to refind this speech act. We describe and reflect on our experiences with crowdsourcing this test collection using the Amazon Mechanical Turk platform. We highlight the challenges of constructing this dataset, including the selection of the data source, design of the crowdsouring task and the specification of queries and relevant items.