7 research outputs found
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
We analyze a word embedding method in supervised tasks. It maps words on a
sphere such that words co-occurring in similar contexts lie closely. The
similarity of contexts is measured by the distribution of substitutes that can
fill them. We compared word embeddings, including more recent representations,
in Named Entity Recognition (NER), Chunking, and Dependency Parsing. We examine
our framework in multilingual dependency parsing as well. The results show that
the proposed method achieves as good as or better results compared to the other
word embeddings in the tasks we investigate. It achieves state-of-the-art
results in multilingual dependency parsing. Word embeddings in 7 languages are
available for public use.Comment: 11 page
Visual Referring Expression Recognition: What Do Systems Actually Learn?
We present an empirical analysis of the state-of-the-art systems for
referring expression recognition -- the task of identifying the object in an
image referred to by a natural language expression -- with the goal of gaining
insight into how these systems reason about language and vision. Surprisingly,
we find strong evidence that even sophisticated and linguistically-motivated
models for this task may ignore the linguistic structure, instead relying on
shallow correlations introduced by unintended biases in the data selection and
annotation process. For example, we show that a system trained and tested on
the input image can achieve a
precision of 71.2% in top-2 predictions. Furthermore, a system that predicts
only the object category given the input can achieve a precision of 84.2% in
top-2 predictions. These surprisingly positive results for what should be
deficient prediction scenarios suggest that careful analysis of what our models
are learning -- and further, how our data is constructed -- is critical as we
seek to make substantive progress on grounded language tasks.Comment: NAACL2018 shor
Using Syntax to Ground Referring Expressions in Natural Images
We introduce GroundNet, a neural network for referring expression recognition
-- the task of localizing (or grounding) in an image the object referred to by
a natural language expression. Our approach to this task is the first to rely
on a syntactic analysis of the input referring expression in order to inform
the structure of the computation graph. Given a parse tree for an input
expression, we explicitly map the syntactic constituents and relationships
present in the tree to a composed graph of neural modules that defines our
architecture for performing localization. This syntax-based approach aids
localization of \textit{both} the target object and auxiliary supporting
objects mentioned in the expression. As a result, GroundNet is more
interpretable than previous methods: we can (1) determine which phrase of the
referring expression points to which object in the image and (2) track how the
localization of the target object is determined by the network. We study this
property empirically by introducing a new set of annotations on the GoogleRef
dataset to evaluate localization of supporting objects. Our experiments show
that GroundNet achieves state-of-the-art accuracy in identifying supporting
objects, while maintaining comparable performance in the localization of target
objects.Comment: AAAI 201
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
We publicly release a new large-scale dataset, called SearchQA, for machine
comprehension, or question-answering. Unlike recently released datasets, such
as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to
reflect a full pipeline of general question-answering. That is, we start not
from an existing article and generate a question-answer pair, but start from an
existing question-answer pair, crawled from J! Archive, and augment it with
text snippets retrieved by Google. Following this approach, we built SearchQA,
which consists of more than 140k question-answer pairs with each pair having
49.6 snippets on average. Each question-answer-context tuple of the SearchQA
comes with additional meta-data such as the snippet's URL, which we believe
will be valuable resources for future research. We conduct human evaluation as
well as test two baseline methods, one simple word selection and the other deep
learning based, on the SearchQA. We show that there is a meaningful gap between
the human and machine performances. This suggests that the proposed dataset
could well serve as a benchmark for question-answering
Addressing Ambiguity in Unsupervised Part-of-Speech Induction with Substitute Vectors
We study substitute vectors to solve the part-of-speech ambiguity problem in an unsupervised setting. Part-of-speech tagging is a crucial preliminary process in many natural language processing applications. Because many words in natural languages have more than one part-of-speech tag, resolving part-of-speech ambiguity is an important task. We claim that partof-speech ambiguity can be solved using substitute vectors. A substitute vector is constructed with possible substitutes of a target word. This study is built on previous work which has proven that word substitutes are very fruitful for part-ofspeech induction. Experiments show that our methodology works for words with high ambiguity.
The AI-KU System at the SPMRL 2013 Shared Task: Unsupervised Features for Dependency Parsing
We propose the use of the word categories and embeddings induced from raw text as auxiliary features in dependency parsing. To induce word features, we make use of contextual, morphologic and orthographic properties of the words. To exploit the contextual information, we make use of substitute words, the most likely substitutes for target words, generated by using a statistical language model. We generate morphologic and orthographic properties of word types in an unsupervised manner. We use a co-occurrence model with these properties to embed words onto a 25dimensional unit sphere. The AI-KU system shows improvements for some of the languages it is trained on for the first Shared Tas
AI-KU: Using Substitute Vectors and Co-Occurrence Modeling for Word Sense Induction and Disambiguation
Word sense induction aims to discover different senses of a word from a corpus by using unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method allows for labeling a word in more than one sense. In contrast to the most common approach which is to apply clustering or graph partitioning on a representation of first or second order co-occurrences of a word, we propose a system that creates a substitute vector for each target word from the most likely substitutes suggested by a statistical language model. Word samples are then taken according to probabilities of these substitutes and the results of the co-occurrence model are clustered. This approach outperforms the other systems on graded word sense induction task in SemEval-2013.