599 research outputs found
Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization
Fast and effective automated indexing is critical for search and personalized
services. Key phrases that consist of one or more words and represent the main
concepts of the document are often used for the purpose of indexing. In this
paper, we investigate the use of additional semantic features and
pre-processing steps to improve automatic key phrase extraction. These features
include the use of signal words and freebase categories. Some of these features
lead to significant improvements in the accuracy of the results. We also
experimented with 2 forms of document pre-processing that we call light
filtering and co-reference normalization. Light filtering removes sentences
from the document, which are judged peripheral to its main content.
Co-reference normalization unifies several written forms of the same named
entity into a unique form. We also needed a "Gold Standard" - a set of labeled
documents for training and evaluation. While the subjective nature of key
phrase selection precludes a true "Gold Standard", we used Amazon's Mechanical
Turk service to obtain a useful approximation. Our data indicates that the
biggest improvements in performance were due to shallow semantic features, news
categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of
deeper semantic features such as Freebase sub-categories was not beneficial by
itself, but in combination with pre-processing, did cause slight improvements
in the nDCG scores.Comment: In 8th International Conference on Language Resources and Evaluation
(LREC 2012
Dependency Grammar Induction with Neural Lexicalization and Big Training Data
We study the impact of big models (in terms of the degree of lexicalization)
and big data (in terms of the training corpus size) on dependency grammar
induction. We experimented with L-DMV, a lexicalized version of Dependency
Model with Valence and L-NDMV, our lexicalized extension of the Neural
Dependency Model with Valence. We find that L-DMV only benefits from very small
degrees of lexicalization and moderate sizes of training corpora. L-NDMV can
benefit from big training data and lexicalization of greater degrees,
especially when enhanced with good model initialization, and it achieves a
result that is competitive with the current state-of-the-art.Comment: EMNLP 201
Large-Scale Goodness Polarity Lexicons for Community Question Answering
We transfer a key idea from the field of sentiment analysis to a new domain:
community question answering (cQA). The cQA task we are interested in is the
following: given a question and a thread of comments, we want to re-rank the
comments so that the ones that are good answers to the question would be ranked
higher than the bad ones. We notice that good vs. bad comments use specific
vocabulary and that one can often predict the goodness/badness of a comment
even ignoring the question, based on the comment contents only. This leads us
to the idea to build a good/bad polarity lexicon as an analogy to the
positive/negative sentiment polarity lexicons, commonly used in sentiment
analysis. In particular, we use pointwise mutual information in order to build
large-scale goodness polarity lexicons in a semi-supervised manner starting
with a small number of initial seeds. The evaluation results show an
improvement of 0.7 MAP points absolute over a very strong baseline and
state-of-the art performance on SemEval-2016 Task 3.Comment: SIGIR '17, August 07-11, 2017, Shinjuku, Tokyo, Japan; Community
Question Answering; Goodness polarity lexicons; Sentiment Analysi
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