158,838 research outputs found
Representation and learning schemes for sentiment analysis.
This thesis identifies four novel techniques of improving the performance of sentiment analysis of text systems. Thes include feature extraction and selection, enrichment of the document representation and exploitation of the ordinal structure of rating classes. The techniques were evaluated on four sentiment-rich corpora, using two well-known classifiers: Support Vector Machines and Na¨ıve Bayes. This thesis proposes the Part-of-Speech Pattern Selector (PPS), which is a novel technique for automatically selecting Part-of-Speech (PoS) patterns. The PPS selects its patterns from a background dataset by use of a number of measures including Document Frequency, Information Gain, and the Chi-Squared Score. Extensive empirical results show that these patterns perform just as well as the manually selected ones. This has important implications in terms of both the cost and the time spent in manual pattern construction. The position of a phrase within a document is shown to have an influence on its sentiment orientation, and that document classification performance can be improved by weighting phrases in this regard. It is, however, also shown to be necessary to sample the distribution of sentiment rich phrases within documents of a given domain prior to adopting a phrase weighting criteria. A key factor in choosing a classifier for an Ordinal Sentiment Classification (OSC) problem is its ability to address ordinal inter-class similarities. Two types of classifiers are investigated: Those that can inherently solve multi-class problems, and those that decompose a multi-class problem into a sequence of binary problems. Empirical results showed the former to be more effective with regard to both mean squared error and classification time performances. Important features in an OSC problem are shown to distribute themselves across similar classes. Most feature selection techniques are ignorant of inter-class similarities and hence easily overlook such features. The Ordinal Smoothing Procedure (OSP), which augments inter-class similarities into the feature selection process, is introduced in this thesis. Empirical results show the OSP to have a positive effect on mean squared error performance
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201
Automated assessment of non-native learner essays: Investigating the role of linguistic features
Automatic essay scoring (AES) refers to the process of scoring free text
responses to given prompts, considering human grader scores as the gold
standard. Writing such essays is an essential component of many language and
aptitude exams. Hence, AES became an active and established area of research,
and there are many proprietary systems used in real life applications today.
However, not much is known about which specific linguistic features are useful
for prediction and how much of this is consistent across datasets. This article
addresses that by exploring the role of various linguistic features in
automatic essay scoring using two publicly available datasets of non-native
English essays written in test taking scenarios. The linguistic properties are
modeled by encoding lexical, syntactic, discourse and error types of learner
language in the feature set. Predictive models are then developed using these
features on both datasets and the most predictive features are compared. While
the results show that the feature set used results in good predictive models
with both datasets, the question "what are the most predictive features?" has a
different answer for each dataset.Comment: Article accepted for publication at: International Journal of
Artificial Intelligence in Education (IJAIED). To appear in early 2017
(journal url: http://www.springer.com/computer/ai/journal/40593
- …