34,959 research outputs found
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers syntax and document structures, and enriches topic
hierarchies with topic relations. Through a series of experiments, we
demonstrate the superiority of hrLDA over existing topic models, especially for
building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the
settings of noisy data sets, which are likely to occur in many practical
scenarios. Our ontology evaluation results show that ontologies extracted from
hrLDA are very competitive with the ontologies created by domain experts
Topic-dependent sentiment analysis of financial blogs
While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches
Comprehensive Review of Opinion Summarization
The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
The number of documents available into Internet moves each day up. For this
reason, processing this amount of information effectively and expressibly
becomes a major concern for companies and scientists. Methods that represent a
textual document by a topic representation are widely used in Information
Retrieval (IR) to process big data such as Wikipedia articles. One of the main
difficulty in using topic model on huge data collection is related to the
material resources (CPU time and memory) required for model estimate. To deal
with this issue, we propose to build topic spaces from summarized documents. In
this paper, we present a study of topic space representation in the context of
big data. The topic space representation behavior is analyzed on different
languages. Experiments show that topic spaces estimated from text summaries are
as relevant as those estimated from the complete documents. The real advantage
of such an approach is the processing time gain: we showed that the processing
time can be drastically reduced using summarized documents (more than 60\% in
general). This study finally points out the differences between thematic
representations of documents depending on the targeted languages such as
English or latin languages.Comment: 16 pages, 4 tables, 8 figure
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