6,532 research outputs found
Dependency relations as source context in phrase-based SMT
The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical
choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and
supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a Dutch—English translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline
Minimal model of associative learning for cross-situational lexicon acquisition
An explanation for the acquisition of word-object mappings is the associative
learning in a cross-situational scenario. Here we present analytical results of
the performance of a simple associative learning algorithm for acquiring a
one-to-one mapping between objects and words based solely on the
co-occurrence between objects and words. In particular, a learning trial in our
learning scenario consists of the presentation of objects together
with a target word, which refers to one of the objects in the context. We find
that the learning times are distributed exponentially and the learning rates
are given by in the case the target
words are sampled randomly and by in the
case they follow a deterministic presentation sequence. This learning
performance is much superior to those exhibited by humans and more realistic
learning algorithms in cross-situational experiments. We show that introduction
of discrimination limitations using Weber's law and forgetting reduce the
performance of the associative algorithm to the human level
SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations
Although deep language representations have become the dominant form of
language featurization in recent years, in many settings it is important to
understand a model's decision-making process. This necessitates not only an
interpretable model but also interpretable features. In particular, language
must be featurized in a way that is interpretable while still characterizing
the original text well. We present SenteCon, a method for introducing human
interpretability in deep language representations. Given a passage of text,
SenteCon encodes the text as a layer of interpretable categories in which each
dimension corresponds to the relevance of a specific category. Our empirical
evaluations indicate that encoding language with SenteCon provides high-level
interpretability at little to no cost to predictive performance on downstream
tasks. Moreover, we find that SenteCon outperforms existing interpretable
language representations with respect to both its downstream performance and
its agreement with human characterizations of the text.Comment: Accepted to Findings of ACL 202
Medical WordNet: A new methodology for the construction and validation of information resources for consumer health
A consumer health information system must be able to comprehend both expert and non-expert medical vocabulary and to map between the two. We describe an ongoing
project to create a new lexical database called Medical WordNet (MWN), consisting of
medically relevant terms used by and intelligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide
medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true. We summarize the MWN / MFN / MBN project, and describe some of its applications
A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature
At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
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
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