Table Of Content

Trees and Forests in Machine Translation Liang Huang City University of New York Joint work with Kevin Knight (ISI), Aravind Joshi (Penn), Haitao Mi and Qun Liu (ICT), 2006--2010 University of Pennsylvania, March 31st, 2015 NLP is Hard • how many interpretations? Aravind Joshi lexical ambiguity I saw her duck Liang Huang (CUNY) 2 NLP is Hard • how many interpretations? Aravind Joshi structural ambiguity I eat sushi with tuna Liang Huang (CUNY) 3 Ambiguity Explosion NLP: ambiguity explosion • how many interpretations? TCS: combinatorial explosion I saw her duck with a telescope in the garden ... ... Liang Huang (CUNY) 4 Unexpected Structural Ambiguity Liang Huang (CUNY) 5 Ambiguity in Translation zi zhu zhong duan 自助终端 self help terminal device help oneself terminating machine translation requires understanding! (ATM, “self-service terminal”) Liang Huang (CUNY) 6 Ambiguity in Translation “domain adaptation” problem in machine learning Liang Huang (CUNY) 7 Ambiguity in Translation liang’s rule: if you see “X carefully” in China, just don’t do it. 小心 NP <=> be aware of NP 小心 VP <=> be careful not to VP Liang Huang (USC) 8 Translate Server Error clear evidence that MT is used in real life. Liang Huang (CUNY) 9 How do people translate? 1. understand the source language sentence 2. generate the target language translation 布什与沙⻰龙举行了会谈 Bùshí yu Shalóng juxíng le huìtán and/ Bush Sharon hold [past.] meeting with “Bush held a meeting with Sharon” Liang Huang (cuny) 10

Description:

Joint work with Kevin Knight (ISI), Aravind Joshi (Penn), Haitao Mi and Qun Liu (ICT), 2006--2010. University of 1. parse high-level language program into a syntax tree. 2. generate . then αβ(e) = α(v) + c(e) + β(u) + β(w).

Guest lecture by Liang Huang - CUNY Queens PDF

50 Pages·2015·2.95 MB·English

by

Checking for file health...

Save to my drive

Quick download

Download

Upgrade Premium

Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.

Preview Guest lecture by Liang Huang - CUNY Queens

Trees and Forests in Machine Translation Liang Huang City University of New York Joint work with Kevin Knight (ISI), Aravind Joshi (Penn), Haitao Mi and Qun Liu (ICT), 2006--2010 University of Pennsylvania, March 31st, 2015 NLP is Hard • how many interpretations? Aravind Joshi lexical ambiguity I saw her duck Liang Huang (CUNY) 2 NLP is Hard • how many interpretations? Aravind Joshi structural ambiguity I eat sushi with tuna Liang Huang (CUNY) 3 Ambiguity Explosion NLP: ambiguity explosion • how many interpretations? TCS: combinatorial explosion I saw her duck with a telescope in the garden ... ... Liang Huang (CUNY) 4 Unexpected Structural Ambiguity Liang Huang (CUNY) 5 Ambiguity in Translation zi zhu zhong duan 自助终端 self help terminal device help oneself terminating machine translation requires understanding! (ATM, “self-service terminal”) Liang Huang (CUNY) 6 Ambiguity in Translation “domain adaptation” problem in machine learning Liang Huang (CUNY) 7 Ambiguity in Translation liang’s rule: if you see “X carefully” in China, just don’t do it. 小心 NP <=> be aware of NP 小心 VP <=> be careful not to VP Liang Huang (USC) 8 Translate Server Error clear evidence that MT is used in real life. Liang Huang (CUNY) 9 How do people translate? 1. understand the source language sentence 2. generate the target language translation 布什与沙⻰龙举行了会谈 Bùshí yu Shalóng juxíng le huìtán and/ Bush Sharon hold [past.] meeting with “Bush held a meeting with Sharon” Liang Huang (cuny) 10

Description:

Joint work with Kevin Knight (ISI), Aravind Joshi (Penn), Haitao Mi and Qun Liu (ICT), 2006--2010. University of 1. parse high-level language program into a syntax tree. 2. generate . then αβ(e) = α(v) + c(e) + β(u) + β(w).

See more

The list of books you might like

Upgrade Premium

Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.