I am interested in the syntax-semantics interface of tense, aspect, and modal expressions with a focus on sign languages, especially an understudied language Turkish Sign Language (TİD). As a sign language linguist, I investigate how nonmanual markers (face and body movements) — the least understood part of sign language grammars — compose the syntax and semantics of these languages. Specifically, I examine if they form a single morpheme with the manual signs and share the labor in the given structure, or if they enter in the composition as a separate morpheme. In my MA thesis, I showed that a perfective nonmanual marker is a distinct morpheme and has different semantic properties than the manual completive sign. I continue my inquiry on nonmanual markers with modal signs and co-occurring nonmanual markers in my dissertation project. I show that nonmanual markers convey the epistemic commitment of the signer rather than being a lexical or a structural part of manual modal signs. To better understand how the signer commitment is affected by the information structure, we examine the realizations of focus in TİD with my collaborator.
Beyond the linguistic studies, I have also collaborated with the colleagues from the computer engineering and communicative disorders departments. We are examining the effects of sign language learning on the visual domain in our perception study. As applications of linguistics knowledge, I worked with engineers to develop a machine translation system from Turkish to TİD in one project. I was also part of a team to improve the machine recognition of the manual signs in TİD.
Beyond the linguistic studies, I have also collaborated with the colleagues from the computer engineering and communicative disorders departments. We are examining the effects of sign language learning on the visual domain in our perception study. As applications of linguistics knowledge, I worked with engineers to develop a machine translation system from Turkish to TİD in one project. I was also part of a team to improve the machine recognition of the manual signs in TİD.