12,645 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
Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems
Recently, the focus of complex networks research has shifted from the
analysis of isolated properties of a system toward a more realistic modeling of
multiple phenomena - multilayer networks. Motivated by the prosperity of
multilayer approach in social, transport or trade systems, we propose the
introduction of multilayer networks for language. The multilayer network of
language is a unified framework for modeling linguistic subsystems and their
structural properties enabling the exploration of their mutual interactions.
Various aspects of natural language systems can be represented as complex
networks, whose vertices depict linguistic units, while links model their
relations. The multilayer network of language is defined by three aspects: the
network construction principle, the linguistic subsystem and the language of
interest. More precisely, we construct a word-level (syntax, co-occurrence and
its shuffled counterpart) and a subword level (syllables and graphemes) network
layers, from five variations of original text (in the modeled language). The
obtained results suggest that there are substantial differences between the
networks structures of different language subsystems, which are hidden during
the exploration of an isolated layer. The word-level layers share structural
properties regardless of the language (e.g. Croatian or English), while the
syllabic subword level expresses more language dependent structural properties.
The preserved weighted overlap quantifies the similarity of word-level layers
in weighted and directed networks. Moreover, the analysis of motifs reveals a
close topological structure of the syntactic and syllabic layers for both
languages. The findings corroborate that the multilayer network framework is a
powerful, consistent and systematic approach to model several linguistic
subsystems simultaneously and hence to provide a more unified view on language
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets.Comment: 8 pages, 7 figures, accepted as a full paper at IJCAI 201
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