Document coherence describes how much sense text makes in terms of its
logical organisation and discourse flow. Even though coherence is a relatively
difficult notion to quantify precisely, it can be approximated automatically.
This type of coherence modelling is not only interesting in itself, but also
useful for a number of other text processing tasks, including Information
Retrieval (IR), where adjusting the ranking of documents according to both
their relevance and their coherence has been shown to increase retrieval
effectiveness [34,37].
The state of the art in unsupervised coherence modelling represents documents
as bipartite graphs of sentences and discourse entities, and then projects
these bipartite graphs into one-mode undirected graphs. However, one-mode
projections may incur significant loss of the information present in the
original bipartite structure. To address this we present three novel graph
metrics that compute document coherence on the original bipartite graph of
sentences and entities. Evaluation on standard settings shows that: (i) one of
our coherence metrics beats the state of the art in terms of coherence
accuracy; and (ii) all three of our coherence metrics improve retrieval
effectiveness because, as closer analysis reveals, they capture aspects of
document quality that go undetected by both keyword-based standard ranking and
by spam filtering. This work contributes document coherence metrics that are
theoretically principled, parameter-free, and useful to IR.

本文提出了三种新的文档连贯度测量指标，应用于文档的双分图以及连贯实体；经过标准测算，其中一项文档连贯度指标实现了最高的准确度，并且这些指标都有效地提高了 IR 的效率，而这些方面是基于关键字的常规排序和垃圾邮件过滤所无法检测到的。

利用实体网格的二分结构进行文档连贯性和检索

Exploiting the Bipartite Structure of Entity Grids for Document  Coherence and Retrieval

Text documents are structured on multiple levels of detail: individual words
are related by syntax, but larger units of text are related by discourse
structure. Existing language models generally fail to account for discourse
structure, but it is crucial if we are to have language models that reward
coherence and generate coherent texts. We present and empirically evaluate a
set of multi-level recurrent neural network language models, called
Document-Context Language Models (DCLM), which incorporate contextual
information both within and beyond the sentence. In comparison with word-level
recurrent neural network language models, the DCLM models obtain slightly
better predictive likelihoods, and considerably better assessments of document
coherence.

文本文件通常按多个层次结构化：单个单词由语法相关联，但更大的文本单元由话语结构相关联。本研究提出并实证评估了一组多级递归神经网络语言模型，称为文档上下文语言模型（DCLM），它们在句子内外都融入上下文信息。与基于单词级的递归神经网络语言模型相比，DCLM 模型预测概率略有提高，文档连贯性评估显著提高。