875 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment
We consider the task of predicting lexical entailment using distributional
vectors. We perform a novel qualitative analysis of one existing model which
was previously shown to only measure the prototypicality of word pairs. We find
that the model strongly learns to identify hypernyms using Hearst patterns,
which are well known to be predictive of lexical relations. We present a novel
model which exploits this behavior as a method of feature extraction in an
iterative procedure similar to Principal Component Analysis. Our model combines
the extracted features with the strengths of other proposed models in the
literature, and matches or outperforms prior work on multiple data sets.Comment: EMNLP 201
Acquisition of Phrase Correspondences using Natural Deduction Proofs
How to identify, extract, and use phrasal knowledge is a crucial problem for
the task of Recognizing Textual Entailment (RTE). To solve this problem, we
propose a method for detecting paraphrases via natural deduction proofs of
semantic relations between sentence pairs. Our solution relies on a graph
reformulation of partial variable unifications and an algorithm that induces
subgraph alignments between meaning representations. Experiments show that our
method can automatically detect various paraphrases that are absent from
existing paraphrase databases. In addition, the detection of paraphrases using
proof information improves the accuracy of RTE tasks.Comment: 11 pages, 4 figures, accepted as long paper of NAACL HLT 201
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is
important to Natural Language Understanding. This paper introduces the task of
identifying if a desire expressed by a subject in a given short piece of text
was fulfilled. We propose various unstructured and structured models that
capture fulfillment cues such as the subject's emotional state and actions. Our
experiments with two different datasets demonstrate the importance of
understanding the narrative and discourse structure to address this task
Term Definitions Help Hypernymy Detection
Existing methods of hypernymy detection mainly rely on statistics over a big
corpus, either mining some co-occurring patterns like "animals such as cats" or
embedding words of interest into context-aware vectors. These approaches are
therefore limited by the availability of a large enough corpus that can cover
all terms of interest and provide sufficient contextual information to
represent their meaning. In this work, we propose a new paradigm, HyperDef, for
hypernymy detection -- expressing word meaning by encoding word definitions,
along with context driven representation. This has two main benefits: (i)
Definitional sentences express (sense-specific) corpus-independent meanings of
words, hence definition-driven approaches enable strong generalization -- once
trained, the model is expected to work well in open-domain testbeds; (ii)
Global context from a large corpus and definitions provide complementary
information for words. Consequently, our model, HyperDef, once trained on
task-agnostic data, gets state-of-the-art results in multiple benchmarksComment: *SEM'2018 camera-read
A large annotated corpus for learning natural language inference
Understanding entailment and contradiction is fundamental to understanding
natural language, and inference about entailment and contradiction is a
valuable testing ground for the development of semantic representations.
However, machine learning research in this area has been dramatically limited
by the lack of large-scale resources. To address this, we introduce the
Stanford Natural Language Inference corpus, a new, freely available collection
of labeled sentence pairs, written by humans doing a novel grounded task based
on image captioning. At 570K pairs, it is two orders of magnitude larger than
all other resources of its type. This increase in scale allows lexicalized
classifiers to outperform some sophisticated existing entailment models, and it
allows a neural network-based model to perform competitively on natural
language inference benchmarks for the first time.Comment: To appear at EMNLP 2015. The data will be posted shortly before the
conference (the week of 14 Sep) at http://nlp.stanford.edu/projects/snli
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
The Fact Extraction and VERification (FEVER) Shared Task
We present the results of the first Fact Extraction and VERification (FEVER)
Shared Task. The task challenged participants to classify whether human-written
factoid claims could be Supported or Refuted using evidence retrieved from
Wikipedia. We received entries from 23 competing teams, 19 of which scored
higher than the previously published baseline. The best performing system
achieved a FEVER score of 64.21%. In this paper, we present the results of the
shared task and a summary of the systems, highlighting commonalities and
innovations among participating systems.Comment: Revised from published version in the proceedings of the FEVER
workshop at EMNLP 201
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations
Recognizing lexical semantic relations between word pairs is an important
task for many applications of natural language processing. One of the
mainstream approaches to this task is to exploit the lexico-syntactic paths
connecting two target words, which reflect the semantic relations of word
pairs. However, this method requires that the considered words co-occur in a
sentence. This requirement is hardly satisfied because of Zipf's law, which
states that most content words occur very rarely. In this paper, we propose
novel methods with a neural model of to solve this problem.
Our proposed model of can be learned in an unsupervised
manner and can generalize the co-occurrences of word pairs and dependency
paths. This model can be used to augment the path data of word pairs that do
not co-occur in the corpus, and extract features capturing relational
information from word pairs. Our experimental results demonstrate that our
methods improve on previous neural approaches based on dependency paths and
successfully solve the focused problem.Comment: 11 pages, NAACL201
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
Modeling hypernymy, such as poodle is-a dog, is an important generalization
aid to many NLP tasks, such as entailment, coreference, relation extraction,
and question answering. Supervised learning from labeled hypernym sources, such
as WordNet, limits the coverage of these models, which can be addressed by
learning hypernyms from unlabeled text. Existing unsupervised methods either do
not scale to large vocabularies or yield unacceptably poor accuracy. This paper
introduces distributional inclusion vector embedding (DIVE), a
simple-to-implement unsupervised method of hypernym discovery via per-word
non-negative vector embeddings which preserve the inclusion property of word
contexts in a low-dimensional and interpretable space. In experimental
evaluations more comprehensive than any previous literature of which we are
aware-evaluating on 11 datasets using multiple existing as well as newly
proposed scoring functions-we find that our method provides up to double the
precision of previous unsupervised embeddings, and the highest average
performance, using a much more compact word representation, and yielding many
new state-of-the-art results.Comment: NAACL 201
- …