6,849 research outputs found
Segmenting broadcast news streams using lexical chains
In this paper we propose a course-grained NLP approach to text segmentation based on the
analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual
units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling
SeLeCT: a lexical cohesion based news story segmentation system
In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between textual units using a linguistic technique called lexical chaining. We evaluate the relative performance of SeLeCT with respect to two other cohesion based segmenters: TextTiling and C99. Using a recently introduced evaluation metric WindowDiff, we contrast the segmentation accuracy of each system on both "spoken" (CNN news transcripts) and "written" (Reuters newswire) news story test sets extracted from the TDT1 corpus
Thematic Annotation: extracting concepts out of documents
Contrarily to standard approaches to topic annotation, the technique used in
this work does not centrally rely on some sort of -- possibly statistical --
keyword extraction. In fact, the proposed annotation algorithm uses a large
scale semantic database -- the EDR Electronic Dictionary -- that provides a
concept hierarchy based on hyponym and hypernym relations. This concept
hierarchy is used to generate a synthetic representation of the document by
aggregating the words present in topically homogeneous document segments into a
set of concepts best preserving the document's content.
This new extraction technique uses an unexplored approach to topic selection.
Instead of using semantic similarity measures based on a semantic resource, the
later is processed to extract the part of the conceptual hierarchy relevant to
the document content. Then this conceptual hierarchy is searched to extract the
most relevant set of concepts to represent the topics discussed in the
document. Notice that this algorithm is able to extract generic concepts that
are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Exploring lexical patterns in text : lexical cohesion analysis with WordNet
We present a system for the linguistic exploration and analysis of lexical cohesion in English texts. Using an electronic thesaurus-like resource, Princeton WordNet, and the Brown Corpus of English, we have implemented a process of annotating text with lexical chains and a graphical user interface for inspection of the annotated text. We describe the system and report on some sample linguistic analyses carried out using the combined thesaurus-corpus resource
Exploring Automated Essay Scoring for Nonnative English Speakers
Automated Essay Scoring (AES) has been quite popular and is being widely
used. However, lack of appropriate methodology for rating nonnative English
speakers' essays has meant a lopsided advancement in this field. In this paper,
we report initial results of our experiments with nonnative AES that learns
from manual evaluation of nonnative essays. For this purpose, we conducted an
exercise in which essays written by nonnative English speakers in test
environment were rated both manually and by the automated system designed for
the experiment. In the process, we experimented with a few features to learn
about nuances linked to nonnative evaluation. The proposed methodology of
automated essay evaluation has yielded a correlation coefficient of 0.750 with
the manual evaluation.Comment: Accepted for publication at EUROPHRAS 201
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