英语好的帮我翻译篇文章，小弟在此谢过

Posted 2023-05-02

Natural Language Processing in Information Retrieval

Many Natural Language Processing (NLP) techniques have been used in Information Retrieval.
The results are not encouraging. Simple methods (stopwording, porter-style stemming,
etc.) usually yield significant improvements, while higher-level processing (chunking,
parsing, word sense disambiguation, etc.) only yield very small improvements or even
a decrease in accuracy. At the same time, higher-level methods increase the processing and
storage cost dramatically. This makes them hard to use on large collections. We review
NLP techniques and come to the conclusion that (a) NLP needs to be optimized for IR in
order to be effective and (b) document retrieval is not an ideal application for NLP, at least
given the current state-of-the-art in NLP. Other IR-related tasks, e.g., question answering
and information extraction, seem to be better suited.
1 Introduction
Many Natural Language Processing (NLP) techniques, including stemming, partof-
speech tagging, compound recognition, de-compounding, chunking, word
sense disambiguation and others, have been used in Information Retrieval (IR).
The core IR task we are investigating here is document retrieval. Several other IR
tasks use very similar techniques, e.g. document clustering, filtering, new event
detection, and link detection, and they can be combined with NLP in a way similar
to document retrieval.
NLP and IR are very different areas of research, and recent major conferences
only have a small number of papers investigating the use of NLP techniques for
information retrieval. The three conferences listed in table 1 had 411 full papers
in total. Only 6 of them (1.5%) explicitly dealt with NLP for retrieval. The percentage
is slightly higher for conferences with a main focus on IR (SIGIR, ECIR:
2.0%) than for conferences with a main focus on NLP (ACL: 1.0%). In most cases,
researchers work on using existing NLP components (stemmers, taggers, . . . ), apply
them to an IR data set and queries, and then use standard IR techniques. This
out-of-the-box use of NLP components that are not geared towards IR might be
one reason why NLP techniques are only moderately successful when compared
to state-of-the art non-NLP retrieval techniques.
The moderate success contradicts the intuition that NLP should help IR, which
is shared by a large number of researchers. This article reviews the research on
combining the two areas and attempts to identify reasons for why NLP has not
brought a breakthrough to IR.

自然语言处理在信息检索

许多自然语言处理（自由党）技术已被用于信息检索。
结果并不令人鼓舞。简单的方法（ stopwording ，波特式的产生，
等）通常是显着提高，而更高级别的处理（块，
剖析，词义消歧等） ，只有产量非常小的改进，甚至
减少准确性。与此同时，更高层次的方法，提高处理和
存储成本显着。这使它们很难使用的大量藏书。我们审查
自然语言处理技术和得出的结论是， （一）的NLP需要在优化的红外
为了有效和（ b ）文献检索不是一个理想的应用自然语言处理，至少
鉴于目前国家最先进的自然语言处理。其他红外有关的任务，例如，答疑
和信息提取，似乎更适合。
1简介
许多自然语言处理（自由党）技术，包括产生， partof -
词性标注，复合承认，日，复利，块，文字
消歧等，已被用于信息检索（ IR ）的。
红外光谱的核心任务，我们正在调查这是文献检索。其他几个红外
任务使用非常类似的技术，例如文档聚类，过滤，新的事件
检测，检测和链接，他们可以自由党合并的方式相似
文件检索。
自由党和红外光谱有很大的不同领域的研究，和最近的主要会议
只有为数不多的论文调查使用自然语言处理技术
信息检索。这三次会议表1中列举了411论文全文
在总。只有6人（ 1.5 ％ ）明确处理的NLP的检索。百分比
略高于会议的一个主要焦点的红外（ SIGIR ， ECIR ：
2.0 ％ ）相比，会议的主要集中在自然语言处理（的ACL ： 1.0 ％ ） 。在大多数情况下，
研究人员工作的组成部分使用现有的NLP （ stemmers ， taggers ， 。 。 。 ） ，适用于
它们的红外光谱数据集和查询，然后使用标准的红外技术。这个
外出盒使用NLP的组成部分，不是针对红外可能
原因之一自然语言处理技术是唯一比较成功相比
向国家最先进的非NLP的检索技术。
温和的成功违背直觉的NLP应有助于红外，其中
共用了大量的研究人员。本文的研究
结合这两个领域，并试图找出原因，为什么没有的NLP
带来了突破，红外。 参考技术A 不知道你又没有邮箱··有的话写个··
我给你发过来··

拜托英语高手,帮我翻译下这篇文章,急用!!!!

Vehicle scheduling is one important step in the hierarchical planning process
in public transportation. The Multiple-Depot Vehicle Scheduling Problem
(MDVSP) is to assign a fleet of vehicles, possibly stationed at several garages, to a given set of passenger trips such that operational, company-specific, technical, and further side constraints are satisfied and the available resources are employed as efficiently as possible. In the last three decades considerable research has gone into the development of academic as well as practice-oriented solution techniques for the A/'P-hard MDVSP and special often polynomially solvable cases of it. Review articles on this topic are, for instance,
Desrosiers/Dumas/Solomon/Soumis (1995), Daduna/Paixäo (1995) and Bussieck/Winter/Zimmermann (1997).
The most successful solution approaches for the MDVSP are based on network flow models and their integer programming analogues. In the liter ature, there are two basic mathematical models of this type: irst, a direct arcoriented model leading to a multicommodity flow problem and, second, a path-oriented model leading to a set partitioning problem. The latter can also be derived from Dantzig-Wolfe decomposition applied to the first. Both approaches lead to large-scale integer programs, and column generation techniques
are required to solve their LP relaxations. We shall explicitly discuss the differences between these two models in Section 3.
We investigate in this paper the solution of the multicommodity flow for mulation. Solution techniques for models of this flavour have been discussed in various articles: Carpaneto/Dell'Amico/Fischetti/Toth (1989) describe an integer LP (ILP) formulation based on an arc-oriented assignment problem with additional path-oriented flow conservation constraints. They apply a so-called "additive lower bounding" procedure to obtain a lower bound for their ILP formulation. Ribeiro/Soumis (1994) show that this additive lower bounding is a special case of Lagrangean relaxation and its corresponding subgradient method. Forbes/Holt/Watts (1994) solve the integer linear programming
formulation of the multicommodity flow model by branch-and-bound. The sizes of the problems that have been solved to optimality in these publications are relatively small involving up to 600 timetabled trips and 3 depots.
The contribution of this paper is the efficient solution of the ILP (de rived from the multicommodity flow formulation) by means of LP column generation techniques. We use a new technique, called Lagrangean pricing, that is based on Lagrangean relaxations of the multicommodity flow model Embedded within a branch-and-cut frame, this method makes it possible to solve problems from practice to proven optimality. Lagrangean pricing has been developed independently at the same time by Fischetti/Vigo (1996)
and Fischetti/Toth (1996)
for solving the Asymmetric Travelling Saleman Problem and the Resource-Constrained Arborescence Problem.

参考技术A My god. 赏我200分都要考虑下