5,667 research outputs found
Annotation graphs as a framework for multidimensional linguistic data analysis
In recent work we have presented a formal framework for linguistic annotation
based on labeled acyclic digraphs. These `annotation graphs' offer a simple yet
powerful method for representing complex annotation structures incorporating
hierarchy and overlap. Here, we motivate and illustrate our approach using
discourse-level annotations of text and speech data drawn from the CALLHOME,
COCONUT, MUC-7, DAMSL and TRAINS annotation schemes. With the help of domain
specialists, we have constructed a hybrid multi-level annotation for a fragment
of the Boston University Radio Speech Corpus which includes the following
levels: segment, word, breath, ToBI, Tilt, Treebank, coreference and named
entity. We show how annotation graphs can represent hybrid multi-level
structures which derive from a diverse set of file formats. We also show how
the approach facilitates substantive comparison of multiple annotations of a
single signal based on different theoretical models. The discussion shows how
annotation graphs open the door to wide-ranging integration of tools, formats
and corpora.Comment: 10 pages, 10 figures, Towards Standards and Tools for Discourse
Tagging, Proceedings of the Workshop. pp. 1-10. Association for Computational
Linguistic
A Formal Framework for Linguistic Annotation
`Linguistic annotation' covers any descriptive or analytic notations applied
to raw language data. The basic data may be in the form of time functions --
audio, video and/or physiological recordings -- or it may be textual. The added
notations may include transcriptions of all sorts (from phonetic features to
discourse structures), part-of-speech and sense tagging, syntactic analysis,
`named entity' identification, co-reference annotation, and so on. While there
are several ongoing efforts to provide formats and tools for such annotations
and to publish annotated linguistic databases, the lack of widely accepted
standards is becoming a critical problem. Proposed standards, to the extent
they exist, have focussed on file formats. This paper focuses instead on the
logical structure of linguistic annotations. We survey a wide variety of
existing annotation formats and demonstrate a common conceptual core, the
annotation graph. This provides a formal framework for constructing,
maintaining and searching linguistic annotations, while remaining consistent
with many alternative data structures and file formats.Comment: 49 page
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Person Search with Natural Language Description
Searching persons in large-scale image databases with the query of natural
language description has important applications in video surveillance. Existing
methods mainly focused on searching persons with image-based or attribute-based
queries, which have major limitations for a practical usage. In this paper, we
study the problem of person search with natural language description. Given the
textual description of a person, the algorithm of the person search is required
to rank all the samples in the person database then retrieve the most relevant
sample corresponding to the queried description. Since there is no person
dataset or benchmark with textual description available, we collect a
large-scale person description dataset with detailed natural language
annotations and person samples from various sources, termed as CUHK Person
Description Dataset (CUHK-PEDES). A wide range of possible models and baselines
have been evaluated and compared on the person search benchmark. An Recurrent
Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to
establish the state-of-the art performance on person search
Research Articles in Simplified HTML: a Web-first format for HTML-based scholarly articles
Purpose. This paper introduces the Research Articles in Simplified HTML (or RASH), which is a Web-first format for writing HTML-based scholarly papers; it is accompanied by the RASH Framework, a set of tools for interacting with RASH-based articles. The paper also presents an evaluation that involved authors and reviewers of RASH articles submitted to the SAVE-SD 2015 and SAVE-SD 2016 workshops.
Design. RASH has been developed aiming to: be easy to learn and use; share scholarly documents (and embedded semantic annotations) through the Web; support its adoption within the existing publishing workflow.
Findings. The evaluation study confirmed that RASH is ready to be adopted in workshops, conferences, and journals and can be quickly learnt by researchers who are familiar with HTML.
Research Limitations. The evaluation study also highlighted some issues in the adoption of RASH, and in general of HTML formats, especially by less technically savvy users. Moreover, additional tools are needed, e.g., for enabling additional conversions from/to existing formats such as OpenXML.
Practical Implications. RASH (and its Framework) is another step towards enabling the definition of formal representations of the meaning of the content of an article, facilitating its automatic discovery, enabling its linking to semantically related articles, providing access to data within the article in actionable form, and allowing integration of data between papers.
Social Implications. RASH addresses the intrinsic needs related to the various users of a scholarly article: researchers (focussing on its content), readers (experiencing new ways for browsing it), citizen scientists (reusing available data formally defined within it through semantic annotations), publishers (using the advantages of new technologies as envisioned by the Semantic Publishing movement).
Value. RASH helps authors to focus on the organisation of their texts, supports them in the task of semantically enriching the content of articles, and leaves all the issues about validation, visualisation, conversion, and semantic data extraction to the various tools developed within its Framework
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