42 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
Key Phrase Extraction of Lightly Filtered Broadcast News
This paper explores the impact of light filtering on automatic key phrase
extraction (AKE) applied to Broadcast News (BN). Key phrases are words and
expressions that best characterize the content of a document. Key phrases are
often used to index the document or as features in further processing. This
makes improvements in AKE accuracy particularly important. We hypothesized that
filtering out marginally relevant sentences from a document would improve AKE
accuracy. Our experiments confirmed this hypothesis. Elimination of as little
as 10% of the document sentences lead to a 2% improvement in AKE precision and
recall. AKE is built over MAUI toolkit that follows a supervised learning
approach. We trained and tested our AKE method on a gold standard made of 8 BN
programs containing 110 manually annotated news stories. The experiments were
conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio
news/programs, running daily, and monitoring 12 TV and 4 radio channels.Comment: In 15th International Conference on Text, Speech and Dialogue (TSD
2012
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across
Dimensions, to investigate the capability of text generation models to act as
implicit clinical knowledge bases and generate free-flow textual explanations
about various health-related conditions across several dimensions. We collect
and present an associated dataset, CHARDat, consisting of explanations about 52
health conditions across three clinical dimensions. We conduct extensive
experiments using BART and T5 along with data augmentation, and perform
automatic, human, and qualitative analyses. We show that while our models can
perform decently, CHARD is very challenging with strong potential for further
exploration
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding
Spoken dialogue systems (SDS) typically require a predefined semantic ontology to train a spoken language understanding (SLU) module. In addition to the anno-tation cost, a key challenge for design-ing such an ontology is to define a coher-ent slot set while considering their com-plex relations. This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utter-ances and semantic elements without the need of corpus annotations. Specifically, our model learns the semantic slots for a domain-specific SDS in an unsupervised fashion, and carries out semantic pars-ing using latent MF techniques. To fur-ther consider the global semantic struc-ture, such as inter-word and inter-slot re-lations, we augment the latent MF-based model with a knowledge graph propaga-tion model based on a slot-based seman-tic graph and a word-based lexical graph. Our experiments show that the proposed MF approaches produce better SLU mod-els that are able to predict semantic slots and word patterns taking into account their relations and domain-specificity in a joint manner.
Conceptual Analysis of Noun Groups in English. Paper submitted to
An expectation-based system, NGP, for parsing English noun groups into the Conceptual Dependency representation is described. The syste