找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Machine Translation: From Research to Real Users; 5th Conference of th Stephen D. Richardson Conference proceedings 2002 Springer-Verlag Be

[復(fù)制鏈接]
樓主: tricuspid-valve
31#
發(fā)表于 2025-3-26 23:58:03 | 只看該作者
Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy ion divergence problem is usually reserved for Transfer and Interlingual MT because it requires a large combination of complex lexical and structural mappings. A major requirement of these approaches is the accessibility of large amounts of explicit symmetric knowledge for both source and target lan
32#
發(fā)表于 2025-3-27 01:57:00 | 只看該作者
33#
發(fā)表于 2025-3-27 05:58:34 | 只看該作者
Merging Example-Based and Statistical Machine Translation: An Experimentbeing able to say that machine translation fully meets the needs of real-life users. In a previous study [.], we have shown how a SMT engine could benefit from terminological resources, especially when translating texts very different from those used to train the system. In the present paper, we dis
34#
發(fā)表于 2025-3-27 09:44:51 | 只看該作者
35#
發(fā)表于 2025-3-27 14:48:12 | 只看該作者
Better Contextual Translation Using Machine Learningf between contextual specificity and general applicability of the mappings, which typically results in conflicting mappings without distinguishing context. We present a machine-learning approach to choosing between such mappings, using classifiers that, in effect, selectively expand the context for
36#
發(fā)表于 2025-3-27 21:16:43 | 只看該作者
Fast and Accurate Sentence Alignment of Bilingual Corporaither on sentence length or word correspondences. Sentence-length-based methods are relatively fast and fairly accurate. Word-correspondence-based methods are generally more accurate but much slower, and usually depend on cognates or a bilingual lexicon. Our method adapts and combines these approach
37#
發(fā)表于 2025-3-28 00:50:24 | 只看該作者
Deriving Semantic Knowledge from Descriptive Texts Using an MT SystemThe KANT system [.,.] was used to analyze input paragraphs, producing sentence-level interlingua representations. The interlinguas were merged to construct a paragraph-level representation, which was used to create a semantic network in Conceptual Graph (CG) [.] format. The interlinguas are also tra
38#
發(fā)表于 2025-3-28 04:45:34 | 只看該作者
39#
發(fā)表于 2025-3-28 09:41:59 | 只看該作者
40#
發(fā)表于 2025-3-28 13:18:05 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 23:50
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
遂溪县| 黄山市| 玛多县| 林芝县| 亚东县| 五华县| 牡丹江市| 清涧县| 南城县| 香格里拉县| 南召县| 三门县| 白山市| 湖北省| 醴陵市| 昆山市| 阳原县| 察哈| 合江县| 大田县| 凤阳县| 沐川县| 平乡县| 舞阳县| 贵定县| 光泽县| 淅川县| 周至县| 鹿泉市| 光山县| 浏阳市| 京山县| 鹤峰县| 麻江县| 区。| 门源| 扎赉特旗| 杭州市| 博兴县| 海宁市| 彩票|