Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Séaghdha, P. Ó"'
Autor:
Aas, Cecilia, Abdelsalam, Hisham, Belousova, Irina, Bhargava, Shruti, Cheng, Jianpeng, Daland, Robert, Driesen, Joris, Flego, Federico, Guigue, Tristan, Johannsen, Anders, Lal, Partha, Lu, Jiarui, Moniz, Joel Ruben Antony, Perkins, Nathan, Piraviperumal, Dhivya, Pulman, Stephen, Séaghdha, Diarmuid Ó, Sun, David Q., Torr, John, Del Vecchio, Marco, Wacker, Jay, Williams, Jason D., Yu, Hong
It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding system that runs on device. In comparison to a server-based assistant, th
Externí odkaz:
http://arxiv.org/abs/2308.03905
Autor:
Cheng, Jianpeng, Agrawal, Devang, Alonso, Hector Martinez, Bhargava, Shruti, Driesen, Joris, Flego, Federico, Ghosh, Shaona, Kaplan, Dain, Kartsaklis, Dimitri, Li, Lin, Piraviperumal, Dhivya, Williams, Jason D, Yu, Hong, Seaghdha, Diarmuid O, Johannsen, Anders
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositio
Externí odkaz:
http://arxiv.org/abs/2010.12770
Autor:
Hendrickx, Iris, Nakov, Preslav, Szpakowicz, Stan, Kozareva, Zornitsa, Séaghdha, Diarmuid Ó, Veale, Tony
Publikováno v:
SemEval-2013
In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via paraphrasing. Given a two-word noun compound, the participating system
Externí odkaz:
http://arxiv.org/abs/1911.10421
Autor:
Hendrickx, Iris, Kim, Su Nam, Kozareva, Zornitsa, Nakov, Preslav, Séaghdha, Diarmuid Ó, Padó, Sebastian, Pennacchiotti, Marco, Romano, Lorenza, Szpakowicz, Stan
Publikováno v:
SemEval-2010
In response to the continuing research interest in computational semantic analysis, we have proposed a new task for SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of nominals. The task is designed to com
Externí odkaz:
http://arxiv.org/abs/1911.10422
Autor:
Vulić, Ivan, Mrkšić, Nikola, Reichart, Roi, Séaghdha, Diarmuid Ó, Young, Steve, Korhonen, Anna
Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar
Externí odkaz:
http://arxiv.org/abs/1706.00377
Autor:
Mrkšić, Nikola, Vulić, Ivan, Séaghdha, Diarmuid Ó, Leviant, Ira, Reichart, Roi, Gašić, Milica, Korhonen, Anna, Young, Steve
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding sem
Externí odkaz:
http://arxiv.org/abs/1706.00374
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This
Externí odkaz:
http://arxiv.org/abs/1606.03777
Autor:
Mrkšić, Nikola, Séaghdha, Diarmuid Ó, Thomson, Blaise, Gašić, Milica, Rojas-Barahona, Lina, Su, Pei-Hao, Vandyke, David, Wen, Tsung-Hsien, Young, Steve
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly av
Externí odkaz:
http://arxiv.org/abs/1603.00892
Autor:
Mrkšić, Nikola, Séaghdha, Diarmuid Ó, Thomson, Blaise, Gašić, Milica, Su, Pei-Hao, Vandyke, David, Wen, Tsung-Hsien, Young, Steve
Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief
Externí odkaz:
http://arxiv.org/abs/1506.07190
Publikováno v:
BMC Bioinformatics, Vol 12, Iss 1, p 212 (2011)
Abstract Background Applications of Natural Language Processing (NLP) technology to biomedical texts have generated significant interest in recent years. In this paper we identify and investigate the phenomenon of linguistic subdomain variation withi
Externí odkaz:
https://doaj.org/article/0dbf8489421d404b8b3c1e323f5d58f4
