Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in the direction of research.

In this paper, FileMaker Pro has been used to create a database in order to evaluate sign language notation systems used for representing hand configurations. The database cited in this paper focuses on child acquisition data, particularly the dataset of one child and one adult productions of the same American Sign Language (ASL) signs produced in a two-year span. The hand configurations in selected signs have been coded using Stokoe notation (Stokoe, Casterline & Croneberg, 1965), the Hamburg Notation System or HamNoSys (Prillwitz et al, 1989), the revised Prosodic Model Handshape Coding system or PM (Eccarius & Brentari, 2008) and Sign Language Phonetic Annotation or SLPA, a notation system that has grown from the Movement-Hold Model (Johnson & Liddell, 2010, 2011a, 2011b, 2012). Data was pulled from ELAN transcripts, organized and notated in a FileMaker Pro database created to investigate the representativeness of each system. Representativeness refers to the ability of the notation system to represent the hand configurations in the dataset. This paper briefly describes the design of the FileMaker Pro database intended to provide both quantitative and qualitative information in order to allow the sign language researcher to examine the representativeness of sign language notation systems.