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Unsettling race and language: Toward a raciolinguistic perspective
This article presents what we term a raciolinguistic perspective, which theorizes the historical and contemporary co-naturalization of language and race. Rather than taking for granted existing categories for parsing and classifying race and language, we seek to understand how and why these categories have been co-naturalized, and to imagine their denaturalization as part of a broader structural project of contesting white supremacy. We explore five key components of a raciolinguistic perspective: (i) historical and contemporary colonial co-naturalizations of race and language; (ii) perceptions of racial and linguistic difference; (iii) regimentations of racial and linguistic categories; (iv) racial and linguistic intersections and assemblages; and (v) contestations of racial and linguistic power formations. These foci reflect our investment in developing a careful theorization of various forms of racial and linguistic inequality on the one hand, and our commitment to the imagination and creation of more just societies on the other. (Race, language ideologies, colonialism, governmentality, enregisterment, structural inequality)*
Journal Article
Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot
Text-to-GraphQL (Text2GraphQL) is a task that converts the user's questions into Graph + QL (Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GraphQL task. This solution uses the Adapter pre-trained by “the linking of GraphQL schemas and the corresponding utterances” as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GraphQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GraphQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.
Journal Article
Natural language processing for similar languages, varieties, and dialects: A survey
There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.
Journal Article
EDITOR'S NOTE
How they are crucial to understanding our shared humanity and yet they've so often been set aside as less significant (more feminine?) than subjects from outside the home and the self. Parsing the difference between traveler and tourist, then, seemed like it might be a useful way to encapsulate or introduce the issue, as this difference might also shed light on the differences between art and commerce, appreciation and appropriation, venturing in and venturing out-and maybe on the literary project in general. Or I could open the door with an intriguing line from Carmen Gimenéz: \"having fallen out of love / with humanity, / I outgrew my leather pants, irony, / nuclear rage.\"
Journal Article
Parsing TTree Formula in Python
Uproot can read ROOT files directly in pure Python but cannot (yet) compute expressions in ROOT’s TTreeFormula expression language. Despite its popularity, this language has only one implementation and no formal specification. In a package called “formulate,” we defined the language’s syntax in standard BNF and parse it with Lark, a fast and modern parsing toolkit in Python. With formulate, users can now convert ROOT TTreeFormula expressions into NumExpr and Awkward Array manipulations. In this contribution, we describe BNF notation and the Look Ahead Left to Right (LALR) parsing algorithm, which scales linearly with expression length. We also present the challenges with interpreting TTreeFormula expressions as a functional language; some function-like forms can’t be expressed as true functions. We also describe the design of the abstract syntax tree that facilitates conversion between the three languages. The formulate package has zero package dependencies, so we are adding it as one of Uproot’s dependencies so that Uproot will be able to use TTreeFormula expressions, whether they are hand-written or embedded in a ROOT file as TTree aliases.
Journal Article
How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis
Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.
Journal Article
FeTaQA: Free-form Table Question Answering
Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based
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pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.
Journal Article
The Datafication of Health
Over the past decade, data-intensive logics and practices have come to affect domains of contemporary life ranging from marketing and policy making to entertainment and education; at every turn, there is evidence of \"datafication\" or the conversion of qualitative aspects of life into quantified data. The datafication of health unfolds on a number of different scales and registers, including data-driven medical research and public health infrastructures, clinical health care, and self-care practices. For the purposes of this review, we focus mainly on the latter two domains, examining how scholars in anthropology, sociology, science and technology studies, and media and communication studies have begun to explore the datafication of clinical and self-care practices. We identify the dominant themes and questions, methodological approaches, and analytical resources of this emerging literature, parsing these under three headings: datafied power, living with data, and data-human mediations
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We conclude by urging scholars to pay closer attention to how datafication is unfolding on the \"other side\" of various digital divides (e.g., financial, technological, geographic), to experiment with applied forms of research and data activism, and to probe links to areas of datafication that are not explicitly related to health.
Journal Article