17 research outputs found
A Learning Approach to Shallow Parsing
A SNoW based learning approach to shallow parsing tasks is presented and
studied experimentally. The approach learns to identify syntactic patterns by
combining simple predictors to produce a coherent inference. Two instantiations
of this approach are studied and experimental results for Noun-Phrases (NP) and
Subject-Verb (SV) phrases that compare favorably with the best published
results are presented. In doing that, we compare two ways of modeling the
problem of learning to recognize patterns and suggest that shallow parsing
patterns are better learned using open/close predictors than using
inside/outside predictors.Comment: LaTex 2e, 11 pages, 2 eps figures, 1 bbl file, uses colacl.st
Memory-Based Shallow Parsing
We present a memory-based learning (MBL) approach to shallow parsing in which
POS tagging, chunking, and identification of syntactic relations are formulated
as memory-based modules. The experiments reported in this paper show
competitive results, the F-value for the Wall Street Journal (WSJ) treebank is:
93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and
79.0% for object detection.Comment: 8 pages, to appear in: Proceedings of the EACL'99 workshop on
Computational Natural Language Learning (CoNLL-99), Bergen, Norway, June 199
Memory-Based Shallow Parsing
We present memory-based learning approaches to shallow parsing and apply
these to five tasks: base noun phrase identification, arbitrary base phrase
recognition, clause detection, noun phrase parsing and full parsing. We use
feature selection techniques and system combination methods for improving the
performance of the memory-based learner. Our approach is evaluated on standard
data sets and the results are compared with that of other systems. This reveals
that our approach works well for base phrase identification while its
application towards recognizing embedded structures leaves some room for
improvement
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Linguistically Motivated Features for Enhanced Back-of-the-Book Indexing
This paper discusses linguistically motivated features for enhanced back-of-the-book indexing
Topical Opinion Retrieval
With a growing amount of subjective content distributed across the Web, there is a need for a domain-independent information retrieval system that would support ad hoc retrieval of documents expressing opinions on a specific topic of the user’s query. While the research area of opinion detection and sentiment analysis has received much attention in the recent years, little research has been done on identifying subjective content targeted at a specific topic, i.e. expressing topical opinion. This thesis presents a novel method for ad hoc retrieval of documents which contain subjective content on the topic of the query. Documents are ranked by the likelihood each document expresses an opinion on a query term, approximated as the likelihood any occurrence of the query term is modified by a subjective adjective. Domain-independent user-based evaluation of the proposed methods was conducted, and shows statistically significant gains over Google ranking as the baseline