Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sadde, Shoval"'
Autor:
Geva, Mor, Caciularu, Avi, Dar, Guy, Roit, Paul, Sadde, Shoval, Shlain, Micah, Tamir, Bar, Goldberg, Yoav
The opaque nature and unexplained behavior of transformer-based language models (LMs) have spurred a wide interest in interpreting their predictions. However, current interpretation methods mostly focus on probing models from outside, executing behav
Externí odkaz:
http://arxiv.org/abs/2204.12130
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory a
Externí odkaz:
http://arxiv.org/abs/2110.07681
Modality is the linguistic ability to describe events with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and m
Externí odkaz:
http://arxiv.org/abs/2106.08037
Autor:
Taub-Tabib, Hillel, Shlain, Micah, Sadde, Shoval, Lahav, Dan, Eyal, Matan, Cohen, Yaara, Goldberg, Yoav
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword q
Externí odkaz:
http://arxiv.org/abs/2006.04148
We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. In contrast to previous attempts to this effect, we introduce a light-weight query language that does not requir
Externí odkaz:
http://arxiv.org/abs/2006.03010
For languages with simple morphology, such as English, automatic annotation pipelines such as spaCy or Stanford's CoreNLP successfully serve projects in academia and the industry. For many morphologically-rich languages (MRLs), similar pipelines show
Externí odkaz:
http://arxiv.org/abs/1908.05453
Publikováno v:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
ACL
ACL
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a91f4ca6a1673e53e0d4ef0510d6c74b