1,421 research outputs found
Extending Topic Models With Syntax and Semantics Relationships
Probabilistic topic modeling is a powerful tool to uncover hidden thematic structure of documents. These hidden structures are useful for extracting concepts of documents and other data mining tasks, such as information retrieval. Latent Dirichlet allocation (LDA), is a generative probabilistic topic model for collections of discrete data such as text corpora. LDA represents documents as a bag-of-words, where the important structure of documents is neglected. In this work, we proposed three extended LDA models that incorporates syntactic and semantic structures of text documents into probabilistic topic models.
Our first proposed topic model enriches text documents with collapsed typed dependency relations to effectively acquire syntactic and semantic dependencies between consecutive and nonconsecutive words of text documents. This representation has several benefits. It captures relations between consecutive and nonconsecutive words of text documents. In addition, the labels of the collapsed typed dependency relations help to eliminate less important relations, i.e., relations involving prepositions. Moreover, in this thesis, we introduced a method to enforce topic similarity to conceptually similar words. As a result, this algorithm leads to more coherent topic distribution over words.
Our second and third proposed generative topic models incorporate term importance into latent topic variables by boosting the probability of important terms and consequently decreasing the probability of less important terms to better reflect the themes of documents. In essence, we assign weights to terms by employing corpus-level and document-level approaches. We incorporate term importance using a nonuniform base measure for an asymmetric prior over topic term distributions in the LDA framework. This leads to better estimates for important terms that occur less frequently in documents. Experimental studies have been conducted to show the effectiveness of our work across a variety of text mining applications.
Furthermore, we employ our topic models to build a personalized content-based news recommender system. Our proposed recommender system eases reading and navigation through online newspapers. In essence, the recommender system acts as filters, delivering only news articles that can be considered relevant to a user. This recommender system has been used by The Globe and Mail, a company that offers most authoritative news in Canada, featuring national and international news
A Dependency-Based Neural Network for Relation Classification
Previous research on relation classification has verified the effectiveness
of using dependency shortest paths or subtrees. In this paper, we further
explore how to make full use of the combination of these dependency
information. We first propose a new structure, termed augmented dependency path
(ADP), which is composed of the shortest dependency path between two entities
and the subtrees attached to the shortest path. To exploit the semantic
representation behind the ADP structure, we develop dependency-based neural
networks (DepNN): a recursive neural network designed to model the subtrees,
and a convolutional neural network to capture the most important features on
the shortest path. Experiments on the SemEval-2010 dataset show that our
proposed method achieves state-of-art results.Comment: This preprint is the full version of a short paper accepted in the
annual meeting of the Association for Computational Linguistics (ACL) 2015
(Beijing, China
Unsupervised Extraction of Representative Concepts from Scientific Literature
This paper studies the automated categorization and extraction of scientific
concepts from titles of scientific articles, in order to gain a deeper
understanding of their key contributions and facilitate the construction of a
generic academic knowledgebase. Towards this goal, we propose an unsupervised,
domain-independent, and scalable two-phase algorithm to type and extract key
concept mentions into aspects of interest (e.g., Techniques, Applications,
etc.). In the first phase of our algorithm we propose PhraseType, a
probabilistic generative model which exploits textual features and limited POS
tags to broadly segment text snippets into aspect-typed phrases. We extend this
model to simultaneously learn aspect-specific features and identify academic
domains in multi-domain corpora, since the two tasks mutually enhance each
other. In the second phase, we propose an approach based on adaptor grammars to
extract fine grained concept mentions from the aspect-typed phrases without the
need for any external resources or human effort, in a purely data-driven
manner. We apply our technique to study literature from diverse scientific
domains and show significant gains over state-of-the-art concept extraction
techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
Most of the online news media outlets rely heavily on the revenues generated
from the clicks made by their readers, and due to the presence of numerous such
outlets, they need to compete with each other for reader attention. To attract
the readers to click on an article and subsequently visit the media site, the
outlets often come up with catchy headlines accompanying the article links,
which lure the readers to click on the link. Such headlines are known as
Clickbaits. While these baits may trick the readers into clicking, in the long
run, clickbaits usually don't live up to the expectation of the readers, and
leave them disappointed.
In this work, we attempt to automatically detect clickbaits and then build a
browser extension which warns the readers of different media sites about the
possibility of being baited by such headlines. The extension also offers each
reader an option to block clickbaits she doesn't want to see. Then, using such
reader choices, the extension automatically blocks similar clickbaits during
her future visits. We run extensive offline and online experiments across
multiple media sites and find that the proposed clickbait detection and the
personalized blocking approaches perform very well achieving 93% accuracy in
detecting and 89% accuracy in blocking clickbaits.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM
Creating a Semantic Graph from Wikipedia
With the continued need to organize and automate the use of data, solutions are needed to transform unstructred text into structred information. By treating dependency grammar functions as programming language functions, this process produces \property maps which connect entities (people, places, events) with snippets of information. These maps are used to construct a semantic graph. By inputting Wikipedia, a large graph of information is produced representing a section of history. The resulting graph allows a user to quickly browse a topic and view the interconnections between entities across history
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.
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