Segmental structure is a common pattern in many types of sequences such as
phrases in human languages. In this paper, we present a probabilistic model for
sequences via their segmentations. The probability of a segmented sequence is
calculated as the product of the probabilities of all its segments, where each
segment is modeled using existing tools such as recurrent neural networks.
Since the segmentation of a sequence is usually unknown in advance, we sum over
all valid segmentations to obtain the final probability for the sequence. An
efficient dynamic programming algorithm is developed for forward and backward
computations without resorting to any approximation. We demonstrate our
approach on text segmentation and speech recognition tasks. In addition to
quantitative results, we also show that our approach can discover meaningful
segments in their respective application contexts.

本研究提出了一种基于分割的概率模型来计算序列的概率，并开发了高效的动态规划算法，能够在文本分割和语音识别任务中发现相关分割，并取得了良好的结果。

基于分割的序列建模

Sequence Modeling via Segmentations

Text segmentation (TS) aims at dividing long text into coherent segments
which reflect the subtopic structure of the text. It is beneficial to many
natural language processing tasks, such as Information Retrieval (IR) and
document summarisation. Current approaches to text segmentation are similar in
that they all use word-frequency metrics to measure the similarity between two
regions of text, so that a document is segmented based on the lexical cohesion
between its words. Various NLP tasks are now moving towards the semantic web
and ontologies, such as ontology-based IR systems, to capture the
conceptualizations associated with user needs and contents. Text segmentation
based on lexical cohesion between words is hence not sufficient anymore for
such tasks. This paper proposes OntoSeg, a novel approach to text segmentation
based on the ontological similarity between text blocks. The proposed method
uses ontological similarity to explore conceptual relations between text
segments and a Hierarchical Agglomerative Clustering (HAC) algorithm to
represent the text as a tree-like hierarchy that is conceptually structured.
The rich structure of the created tree further allows the segmentation of text
in a linear fashion at various levels of granularity. The proposed method was
evaluated on a wellknown dataset, and the results show that using ontological
similarity in text segmentation is very promising. Also we enhance the proposed
method by combining ontological similarity with lexical similarity and the
results show an enhancement of the segmentation quality.

本文提出了一种基于本体相似性的文本分割方法，使用 Hierarchical Agglomerative Clustering 算法生成一种树形分层结构，用于表征文本的概念结构，从而实现在不同粒度级别上的线性文本分割，实验证明该方法具有很高的分割质量。

OntoSeg: 使用本体相似性的文本分割新方法

OntoSeg: a Novel Approach to Text Segmentation using Ontological  Similarity

Automatic segmentation of text into minimal content-bearing units is an
unsolved problem even for languages like English. Spaces between words offer an
easy first approximation, but this approximation is not good enough for machine
translation (MT), where many word sequences are not translated word-for-word.
This paper presents an efficient automatic method for discovering sequences of
words that are translated as a unit. The method proceeds by comparing pairs of
statistical translation models induced from parallel texts in two languages. It
can discover hundreds of non-compositional compounds on each iteration, and
constructs longer compounds out of shorter ones. Objective evaluation on a
simple machine translation task has shown the method's potential to improve the
quality of MT output. The method makes few assumptions about the data, so it
can be applied to parallel data other than parallel texts, such as word
spellings and pronunciations.

本文提出一种基于翻译模型的文本分割方法，用于识别翻译时作为一个单元的语言序列，在机器翻译任务中有着不错的性能表现，不依赖于特定数据来源，可应用于多种平行语料数据。

自动发现平行数据中的非组合性复合词

Automatic Discovery of Non-Compositional Compounds in Parallel Data

This paper discusses the problem of learning language from unprocessed text
and speech signals, concentrating on the problem of learning a lexicon. In
particular, it argues for a representation of language in which linguistic
parameters like words are built by perturbing a composition of existing
parameters. The power of this representation is demonstrated by several
examples in text segmentation and compression, acquisition of a lexicon from
raw speech, and the acquisition of mappings between text and artificial
representations of meaning.

探讨如何从未经处理的文本和语音信号中学习语言，着重于学习词汇表的问题；提出了一种语言表示方法，通过扰动现有参数的组合来建立语言参数，展示了该表示方法在文本分割和压缩、从原始语音中获取词汇表以及文本和人工意义表示之间的映射学习中的应用。