9,007 research outputs found
Treebank-based acquisition of wide-coverage, probabilistic LFG resources: project overview, results and evaluation
This paper presents an overview of a project to acquire wide-coverage, probabilistic Lexical-Functional Grammar
(LFG) resources from treebanks. Our approach is based on an automatic annotation algorithm that annotates “raw” treebank trees with LFG f-structure information approximating to basic predicate-argument/dependency structure. From the f-structure-annotated treebank
we extract probabilistic unification grammar resources. We present the annotation algorithm, the extraction of
lexical information and the acquisition of wide-coverage and robust PCFG-based LFG approximations including
long-distance dependency resolution.
We show how the methodology can be applied to multilingual, treebank-based unification grammar acquisition. Finally
we show how simple (quasi-)logical forms can be derived automatically from the f-structures generated for the treebank trees
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An improved hidden vector state model approach and its adaptation in extracting protein interaction information from biomedical literature
Large quantity of knowledge, which is important for biological researchers to unveil the mechanism of life, often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and full parsing, have been proposed especially for biomedical applications. In this paper, we present an information extraction system employing a semantic parser using the Hidden Vector State (HVS) model for protein-protein interactions. We found that it performed better than other established statistical methods and achieved 58.3% and 76.8% in recall and precision respectively. Moreover, the pure data-driven HVS model can be easily adapted to other domains, which is rarely mentioned and possessed by other approaches. Experimental results prove that the model trained on one domain can still generate satisfactory results when shifting to another domain with a small amount of adaptation training data
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Beyond the existing single-person and multiple-person human parsing tasks in
static images, this paper makes the first attempt to investigate a more
realistic video instance-level human parsing that simultaneously segments out
each person instance and parses each instance into more fine-grained parts
(e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding
Network (ATEN) that alternatively performs temporal encoding among key frames
and flow-guided feature propagation from other consecutive frames between two
key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the
instance-level parsing result for each key frame, which integrates both the
global human parsing and instance-level human segmentation into a unified
model. To balance between accuracy and efficiency, the flow-guided feature
propagation is used to directly parse consecutive frames according to their
identified temporal consistency with key frames. On the other hand, ATEN
leverages the convolution gated recurrent units (convGRU) to exploit temporal
changes over a series of key frames, which are further used to facilitate the
frame-level instance-level parsing. By alternatively performing direct feature
propagation between consistent frames and temporal encoding network among key
frames, our ATEN achieves a good balance between frame-level accuracy and time
efficiency, which is a common crucial problem in video object segmentation
research. To demonstrate the superiority of our ATEN, extensive experiments are
conducted on the most popular video segmentation benchmark (DAVIS) and a newly
collected Video Instance-level Parsing (VIP) dataset, which is the first video
instance-level human parsing dataset comprised of 404 sequences and over 20k
frames with instance-level and pixel-wise annotations.Comment: To appear in ACM MM 2018. Code link:
https://github.com/HCPLab-SYSU/ATEN. Dataset link: http://sysu-hcp.net/li
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
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
International audienceThis paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that had shown the effectiveness of adversarial learning for domain generalization in the context of semantic parsing of encyclopedic written documents, we propose to extend this approach to elocutionary style generalization. The underlying question throughout this study is whether adversarial learning can be used to combine data from different sources and train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations as well as automatic speech recognition errors. The proposed strategy is evaluated on a French corpus of encyclopedic written documents and a smaller corpus of radio podcast transcriptions, both annotated with a FrameNet paradigm. We show that adversarial learning increases all models generalization capabilities both on manual and automatic speech transcription as well as on encyclopedic data
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
Compositional embedding models build a representation (or embedding) for a
linguistic structure based on its component word embeddings. We propose a
Feature-rich Compositional Embedding Model (FCM) for relation extraction that
is expressive, generalizes to new domains, and is easy-to-implement. The key
idea is to combine both (unlexicalized) hand-crafted features with learned word
embeddings. The model is able to directly tackle the difficulties met by
traditional compositional embeddings models, such as handling arbitrary types
of sentence annotations and utilizing global information for composition. We
test the proposed model on two relation extraction tasks, and demonstrate that
our model outperforms both previous compositional models and traditional
feature rich models on the ACE 2005 relation extraction task, and the SemEval
2010 relation classification task. The combination of our model and a
log-linear classifier with hand-crafted features gives state-of-the-art
results.Comment: 12 pages for EMNLP 201
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In this work we define a new methodology that resolves this
bottleneck and provides large scale supervised reading comprehension data. This
allows us to develop a class of attention based deep neural networks that learn
to read real documents and answer complex questions with minimal prior
knowledge of language structure.Comment: Appears in: Advances in Neural Information Processing Systems 28
(NIPS 2015). 14 pages, 13 figure
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