101,884 research outputs found
Pattern Matching and Discourse Processing in Information Extraction from Japanese Text
Information extraction is the task of automatically picking up information of
interest from an unconstrained text. Information of interest is usually
extracted in two steps. First, sentence level processing locates relevant
pieces of information scattered throughout the text; second, discourse
processing merges coreferential information to generate the output. In the
first step, pieces of information are locally identified without recognizing
any relationships among them. A key word search or simple pattern search can
achieve this purpose. The second step requires deeper knowledge in order to
understand relationships among separately identified pieces of information.
Previous information extraction systems focused on the first step, partly
because they were not required to link up each piece of information with other
pieces. To link the extracted pieces of information and map them onto a
structured output format, complex discourse processing is essential. This paper
reports on a Japanese information extraction system that merges information
using a pattern matcher and discourse processor. Evaluation results show a high
level of system performance which approaches human performance.Comment: See http://www.jair.org/ for any accompanying file
Extracting information from short messages
Much currently transmitted information takes the form of e-mails or SMS text messages and so extracting information from such short messages is increasingly important. The words in a message can be partitioned into the syntactic structure, terms from the domain of discourse and the data being transmitted. This paper describes a light-weight Information Extraction component which uses pattern matching to separate the three aspects: the structure is supplied as a template; domain terms are the metadata of a data source (or their synonyms), and data is extracted as those words matching placeholders in the templates
Wrapper Maintenance: A Machine Learning Approach
The proliferation of online information sources has led to an increased use
of wrappers for extracting data from Web sources. While most of the previous
research has focused on quick and efficient generation of wrappers, the
development of tools for wrapper maintenance has received less attention. This
is an important research problem because Web sources often change in ways that
prevent the wrappers from extracting data correctly. We present an efficient
algorithm that learns structural information about data from positive examples
alone. We describe how this information can be used for two wrapper maintenance
applications: wrapper verification and reinduction. The wrapper verification
system detects when a wrapper is not extracting correct data, usually because
the Web source has changed its format. The reinduction algorithm automatically
recovers from changes in the Web source by identifying data on Web pages so
that a new wrapper may be generated for this source. To validate our approach,
we monitored 27 wrappers over a period of a year. The verification algorithm
correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes,
resulting in precision of 0.73 and recall of 0.95. We validated the reinduction
algorithm on ten Web sources. We were able to successfully reinduce the
wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data
extraction task
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines
to quickly grasp the informative and distinctive information of an entity.
Entities in most knowledge graphs (KGs) still lack such descriptions, thus
calling for automatic methods to supplement such information. However, existing
generative methods either overlook the grammatical structure or make factual
mistakes in generated texts. To solve these problems, we propose a
head-modifier template-based method to ensure the readability and data fidelity
of generated type descriptions. We also propose a new dataset and two automatic
metrics for this task. Experiments show that our method improves substantially
compared with baselines and achieves state-of-the-art performance on both
datasets.Comment: ACL 201
Information extraction
In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates
Mixing representation levels: The hybrid approach to automatic text generation
Natural language generation systems (NLG) map non-linguistic representations
into strings of words through a number of steps using intermediate
representations of various levels of abstraction. Template based systems, by
contrast, tend to use only one representation level, i.e. fixed strings, which
are combined, possibly in a sophisticated way, to generate the final text.
In some circumstances, it may be profitable to combine NLG and template based
techniques. The issue of combining generation techniques can be seen in more
abstract terms as the issue of mixing levels of representation of different
degrees of linguistic abstraction. This paper aims at defining a reference
architecture for systems using mixed representations. We argue that mixed
representations can be used without abandoning a linguistically grounded
approach to language generation.Comment: 6 page
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