15,944 research outputs found
Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application
We present two novel models of document coherence and their application to
information retrieval (IR). Both models approximate document coherence using
discourse entities, e.g. the subject or object of a sentence. Our first model
views text as a Markov process generating sequences of discourse entities
(entity n-grams); we use the entropy of these entity n-grams to approximate the
rate at which new information appears in text, reasoning that as more new words
appear, the topic increasingly drifts and text coherence decreases. Our second
model extends the work of Guinaudeau & Strube [28] that represents text as a
graph of discourse entities, linked by different relations, such as their
distance or adjacency in text. We use several graph topology metrics to
approximate different aspects of the discourse flow that can indicate
coherence, such as the average clustering or betweenness of discourse entities
in text. Experiments with several instantiations of these models show that: (i)
our models perform on a par with two other well-known models of text coherence
even without any parameter tuning, and (ii) reranking retrieval results
according to their coherence scores gives notable performance gains, confirming
a relation between document coherence and relevance. This work contributes two
novel models of document coherence, the application of which to IR complements
recent work in the integration of document cohesiveness or comprehensibility to
ranking [5, 56]
Probabilistic approaches for modeling text structure and their application to text-to-text generation
Since the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output. However, traditional knowledge-intensive approaches have been of limited utility in addressing this problem since they cannot be effectively scaled to operate in domain-independent, large-scale applications. Due to this difficulty, existing text-to-text generation systems rarely rely on such structural information when producing an output text. Consequently, texts generated by these methods do not match the quality of those written by humans – they are often fraught with severe coherence violations and disfluencies.
In this chapter, I will present probabilistic models of document structure that can be effectively learned from raw document collections. This feature distinguishes these new models from traditional knowledge intensive approaches used in symbolic concept-to-text generation. Our results demonstrate that these probabilistic models can be directly applied to content organization, and suggest that these models can prove useful in an even broader range of text-to-text applications than we have considered here.National Science Foundation (U.S.) (CAREER grant IIS- 0448168)Microsoft Research. New Faculty Fellowshi
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Automatic summarising: factors and directions
This position paper suggests that progress with automatic summarising demands
a better research methodology and a carefully focussed research strategy. In
order to develop effective procedures it is necessary to identify and respond
to the context factors, i.e. input, purpose, and output factors, that bear on
summarising and its evaluation. The paper analyses and illustrates these
factors and their implications for evaluation. It then argues that this
analysis, together with the state of the art and the intrinsic difficulty of
summarising, imply a nearer-term strategy concentrating on shallow, but not
surface, text analysis and on indicative summarising. This is illustrated with
current work, from which a potentially productive research programme can be
developed
Individual and Domain Adaptation in Sentence Planning for Dialogue
One of the biggest challenges in the development and deployment of spoken
dialogue systems is the design of the spoken language generation module. This
challenge arises from the need for the generator to adapt to many features of
the dialogue domain, user population, and dialogue context. A promising
approach is trainable generation, which uses general-purpose linguistic
knowledge that is automatically adapted to the features of interest, such as
the application domain, individual user, or user group. In this paper we
present and evaluate a trainable sentence planner for providing restaurant
information in the MATCH dialogue system. We show that trainable sentence
planning can produce complex information presentations whose quality is
comparable to the output of a template-based generator tuned to this domain. We
also show that our method easily supports adapting the sentence planner to
individuals, and that the individualized sentence planners generally perform
better than models trained and tested on a population of individuals. Previous
work has documented and utilized individual preferences for content selection,
but to our knowledge, these results provide the first demonstration of
individual preferences for sentence planning operations, affecting the content
order, discourse structure and sentence structure of system responses. Finally,
we evaluate the contribution of different feature sets, and show that, in our
application, n-gram features often do as well as features based on higher-level
linguistic representations
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
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