9,807 research outputs found
Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
Many social Web sites allow users to annotate the content with descriptive
metadata, such as tags, and more recently to organize content hierarchically.
These types of structured metadata provide valuable evidence for learning how a
community organizes knowledge. For instance, we can aggregate many personal
hierarchies into a common taxonomy, also known as a folksonomy, that will aid
users in visualizing and browsing social content, and also to help them in
organizing their own content. However, learning from social metadata presents
several challenges, since it is sparse, shallow, ambiguous, noisy, and
inconsistent. We describe an approach to folksonomy learning based on
relational clustering, which exploits structured metadata contained in personal
hierarchies. Our approach clusters similar hierarchies using their structure
and tag statistics, then incrementally weaves them into a deeper, bushier tree.
We study folksonomy learning using social metadata extracted from the
photo-sharing site Flickr, and demonstrate that the proposed approach addresses
the challenges. Moreover, comparing to previous work, the approach produces
larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on
Knowledge Discovery and Data Mining(KDD) 201
XLIndy: interactive recognition and information extraction in spreadsheets
Over the years, spreadsheets have established their presence in many domains, including business, government, and science. However, challenges arise due to spreadsheets being partially-structured and carrying implicit (visual and textual) information. This translates into a bottleneck, when it comes to automatic analysis and extraction of information. Therefore, we present XLIndy, a Microsoft Excel add-in with a machine learning back-end, written in Python. It showcases our novel methods for layout inference and table recognition in spreadsheets. For a selected task and method, users can visually inspect the results, change configurations, and compare different runs. This enables iterative fine-tuning. Additionally, users can manually revise the predicted layout and tables, and subsequently save them as annotations. The latter is used to measure performance and (re-)train classifiers. Finally, data in the recognized tables can be extracted for further processing. XLIndy supports several standard formats, such as CSV and JSON.Peer ReviewedPostprint (author's final draft
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
We propose a distance supervised relation extraction approach for
long-tailed, imbalanced data which is prevalent in real-world settings. Here,
the challenge is to learn accurate "few-shot" models for classes existing at
the tail of the class distribution, for which little data is available.
Inspired by the rich semantic correlations between classes at the long tail and
those at the head, we take advantage of the knowledge from data-rich classes at
the head of the distribution to boost the performance of the data-poor classes
at the tail. First, we propose to leverage implicit relational knowledge among
class labels from knowledge graph embeddings and learn explicit relational
knowledge using graph convolution networks. Second, we integrate that
relational knowledge into relation extraction model by coarse-to-fine
knowledge-aware attention mechanism. We demonstrate our results for a
large-scale benchmark dataset which show that our approach significantly
outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Extraction from raw text to a knowledge base of entities and fine-grained
types is often cast as prediction into a flat set of entity and type labels,
neglecting the rich hierarchies over types and entities contained in curated
ontologies. Previous attempts to incorporate hierarchical structure have
yielded little benefit and are restricted to shallow ontologies. This paper
presents new methods using real and complex bilinear mappings for integrating
hierarchical information, yielding substantial improvement over flat
predictions in entity linking and fine-grained entity typing, and achieving new
state-of-the-art results for end-to-end models on the benchmark FIGER dataset.
We also present two new human-annotated datasets containing wide and deep
hierarchies which we will release to the community to encourage further
research in this direction: MedMentions, a collection of PubMed abstracts in
which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet,
which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k
entity types. In experiments on all three datasets we show substantial gains
from hierarchy-aware training.Comment: ACL 201
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
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