2,880 research outputs found
Word Embeddings for Entity-annotated Texts
Learned vector representations of words are useful tools for many information
retrieval and natural language processing tasks due to their ability to capture
lexical semantics. However, while many such tasks involve or even rely on named
entities as central components, popular word embedding models have so far
failed to include entities as first-class citizens. While it seems intuitive
that annotating named entities in the training corpus should result in more
intelligent word features for downstream tasks, performance issues arise when
popular embedding approaches are naively applied to entity annotated corpora.
Not only are the resulting entity embeddings less useful than expected, but one
also finds that the performance of the non-entity word embeddings degrades in
comparison to those trained on the raw, unannotated corpus. In this paper, we
investigate approaches to jointly train word and entity embeddings on a large
corpus with automatically annotated and linked entities. We discuss two
distinct approaches to the generation of such embeddings, namely the training
of state-of-the-art embeddings on raw-text and annotated versions of the
corpus, as well as node embeddings of a co-occurrence graph representation of
the annotated corpus. We compare the performance of annotated embeddings and
classical word embeddings on a variety of word similarity, analogy, and
clustering evaluation tasks, and investigate their performance in
entity-specific tasks. Our findings show that it takes more than training
popular word embedding models on an annotated corpus to create entity
embeddings with acceptable performance on common test cases. Based on these
results, we discuss how and when node embeddings of the co-occurrence graph
representation of the text can restore the performance.Comment: This paper is accepted in 41st European Conference on Information
Retrieva
Modelling the Lexicon in Unsupervised Part of Speech Induction
Automatically inducing the syntactic part-of-speech categories for words in
text is a fundamental task in Computational Linguistics. While the performance
of unsupervised tagging models has been slowly improving, current
state-of-the-art systems make the obviously incorrect assumption that all
tokens of a given word type must share a single part-of-speech tag. This
one-tag-per-type heuristic counters the tendency of Hidden Markov Model based
taggers to over generate tags for a given word type. However, it is clearly
incompatible with basic syntactic theory. In this paper we extend a
state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model
of the lexicon. In doing so we are able to incorporate a soft bias towards
inducing few tags per type. We develop a particle filter for drawing samples
from the posterior of our model and present empirical results that show that
our model is competitive with and faster than the state-of-the-art without
making any unrealistic restrictions.Comment: To be presented at the 14th Conference of the European Chapter of the
Association for Computational Linguistic
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however
commonly used Markov models are unable to adequately describe these phenomena
due to their strong locality assumptions. We propose a novel hierarchical model
for structured prediction over sequences and trees which exploits global
context by conditioning each generation decision on an unbounded context of
prior decisions. This builds on the success of Markov models but without
imposing a fixed bound in order to better represent global phenomena. To
facilitate learning of this large and unbounded model, we use a hierarchical
Pitman-Yor process prior which provides a recursive form of smoothing. We
propose prediction algorithms based on A* and Markov Chain Monte Carlo
sampling. Empirical results demonstrate the potential of our model compared to
baseline finite-context Markov models on part-of-speech tagging and syntactic
parsing
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Learning Word Representations from Relational Graphs
Attributes of words and relations between two words are central to numerous
tasks in Artificial Intelligence such as knowledge representation, similarity
measurement, and analogy detection. Often when two words share one or more
attributes in common, they are connected by some semantic relations. On the
other hand, if there are numerous semantic relations between two words, we can
expect some of the attributes of one of the words to be inherited by the other.
Motivated by this close connection between attributes and relations, given a
relational graph in which words are inter- connected via numerous semantic
relations, we propose a method to learn a latent representation for the
individual words. The proposed method considers not only the co-occurrences of
words as done by existing approaches for word representation learning, but also
the semantic relations in which two words co-occur. To evaluate the accuracy of
the word representations learnt using the proposed method, we use the learnt
word representations to solve semantic word analogy problems. Our experimental
results show that it is possible to learn better word representations by using
semantic semantics between words.Comment: AAAI 201
Misspelling Oblivious Word Embeddings
In this paper we present a method to learn word embeddings that are resilient
to misspellings. Existing word embeddings have limited applicability to
malformed texts, which contain a non-negligible amount of out-of-vocabulary
words. We propose a method combining FastText with subwords and a supervised
task of learning misspelling patterns. In our method, misspellings of each word
are embedded close to their correct variants. We train these embeddings on a
new dataset we are releasing publicly. Finally, we experimentally show the
advantages of this approach on both intrinsic and extrinsic NLP tasks using
public test sets.Comment: 9 Page
Combining independent modules to solve multiple-choice synonym and analogy problems
Existing statistical approaches to natural language problems are very
coarse approximations to the true complexity of language processing.
As such, no single technique will be best for all problem instances.
Many researchers are examining ensemble methods that combine the
output of successful, separately developed modules to create more
accurate solutions. This paper examines three merging rules for
combining probability distributions: the well known mixture rule, the
logarithmic rule, and a novel product rule. These rules were applied
with state-of-the-art results to two problems commonly used to assess
human mastery of lexical semantics -- synonym questions and analogy
questions. All three merging rules result in ensembles that are more
accurate than any of their component modules. The differences among the
three rules are not statistically significant, but it is suggestive
that the popular mixture rule is not the best rule for either of the
two problems
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