513 research outputs found
Don't Blame Distributional Semantics if it can't do Entailment
Distributional semantics has had enormous empirical success in Computational
Linguistics and Cognitive Science in modeling various semantic phenomena, such
as semantic similarity, and distributional models are widely used in
state-of-the-art Natural Language Processing systems. However, the theoretical
status of distributional semantics within a broader theory of language and
cognition is still unclear: What does distributional semantics model? Can it
be, on its own, a fully adequate model of the meanings of linguistic
expressions? The standard answer is that distributional semantics is not fully
adequate in this regard, because it falls short on some of the central aspects
of formal semantic approaches: truth conditions, entailment, reference, and
certain aspects of compositionality. We argue that this standard answer rests
on a misconception: These aspects do not belong in a theory of expression
meaning, they are instead aspects of speaker meaning, i.e., communicative
intentions in a particular context. In a slogan: words do not refer, speakers
do. Clearing this up enables us to argue that distributional semantics on its
own is an adequate model of expression meaning. Our proposal sheds light on the
role of distributional semantics in a broader theory of language and cognition,
its relationship to formal semantics, and its place in computational models.Comment: To appear in Proceedings of the 13th International Conference on
Computational Semantics (IWCS 2019), Gothenburg, Swede
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Identifying lexical relationships and entailments with distributional semantics
Many modern efforts in Natural Language Understanding depend on rich and powerful semantic representations of words. Systems for sophisticated logical and textual reasoning often depend heavily on lexical resources to provide critical information about relationships between words, but these lexical resources are expensive to create and maintain, and are never fully comprehensive. Distributional Semantics has long offered methods for automatically inducing meaning representations from large corpora, with little or no annotation efforts. The resulting representations are valuable proxies of semantic similarity, but simply knowing two words are similar cannot tell us their relationship, or whether one entails the other.
In this thesis, we consider how methods from Distributional Semantics may be applied to the difficult task of lexical entailment, where one must predict whether one word implies another. We approach this by showing contributions in areas of hypernymy detection, lexical relationship prediction, lexical substitution, and textual entailment. We propose novel experimental setups, models, analysis, and interpretations, which ultimate provide us with a better understanding of both the nature of lexical entailment, as well as the information available within distributional representations.Computer Science
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Following the major success of neural language models (LMs) such as BERT or
GPT-2 on a variety of language understanding tasks, recent work focused on
injecting (structured) knowledge from external resources into these models.
While on the one hand, joint pretraining (i.e., training from scratch, adding
objectives based on external knowledge to the primary LM objective) may be
prohibitively computationally expensive, post-hoc fine-tuning on external
knowledge, on the other hand, may lead to the catastrophic forgetting of
distributional knowledge. In this work, we investigate models for complementing
the distributional knowledge of BERT with conceptual knowledge from ConceptNet
and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using
adapter training. While overall results on the GLUE benchmark paint an
inconclusive picture, a deeper analysis reveals that our adapter-based models
substantially outperform BERT (up to 15-20 performance points) on inference
tasks that require the type of conceptual knowledge explicitly present in
ConceptNet and OMCS
Distributional Measures of Semantic Distance: A Survey
The ability to mimic human notions of semantic distance has widespread
applications. Some measures rely only on raw text (distributional measures) and
some rely on knowledge sources such as WordNet. Although extensive studies have
been performed to compare WordNet-based measures with human judgment, the use
of distributional measures as proxies to estimate semantic distance has
received little attention. Even though they have traditionally performed poorly
when compared to WordNet-based measures, they lay claim to certain uniquely
attractive features, such as their applicability in resource-poor languages and
their ability to mimic both semantic similarity and semantic relatedness.
Therefore, this paper presents a detailed study of distributional measures.
Particular attention is paid to flesh out the strengths and limitations of both
WordNet-based and distributional measures, and how distributional measures of
distance can be brought more in line with human notions of semantic distance.
We conclude with a brief discussion of recent work on hybrid measures
The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations
The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims
at providing a common benchmark for testing current corpus-based methods for the identifica-
tion of lexical semantic relations (
synonymy
,
antonymy
,
hypernymy
,
part-whole meronymy
) and
at gaining a better understanding of their respective strengths and weaknesses. The shared task
uses a challenging dataset extracted from EVALution 1.0 (Santus et al., 2015b), which contains
word pairs holding the above-mentioned relations as well as semantically unrelated control items
(
random
). The task is split into two subtasks: (i) identification of related word pairs vs. unre-
lated ones; (ii) classification of the word pairs according to their semantic relation. This paper
describes the subtasks, the dataset, the evaluation metrics, the seven participating systems and
their results. The best performing system in subtask 1 is GHHH (
F
1
= 0
.
790
), while the best
system in subtask 2 is LexNet (
F
1
= 0
.
445
). The dataset and the task description are available at
https://sites.google.com/site/cogalex2016/home/shared-task
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin
Specializing distributional vectors of allwords for lexical entailment
Semantic specialization methods fine-tune distributional word vectors using lexical knowledge from external resources (e.g., WordNet) to accentuate a particular relation between words. However, such post-processing methods suffer from limited coverage as they affect only vectors of words seen in the external resources. We present the first postprocessing method that specializes vectors of all vocabulary words – including those unseen in the resources – for the asymmetric relation of lexical entailment (LE) (i.e., hyponymyhypernymy relation). Leveraging a partially LE-specialized distributional space, our POSTLE (i.e., post-specialization for LE) model learns an explicit global specialization function, allowing for specialization of vectors of unseen words, as well as word vectors from other languages via cross-lingual transfer. We capture the function as a deep feedforward neural network: its objective re-scales vector norms to reflect the concept hierarchy while simultaneously attracting hyponymyhypernymy pairs to better reflect semantic similarity. An extended model variant augments the basic architecture with an adversarial discriminator. We demonstrate the usefulness and versatility of POSTLE models with different input distributional spaces in different scenarios (monolingual LE and zero-shot cross-lingual LE transfer) and tasks (binary and graded LE). We report consistent gains over state-of-the-art LE-specialization methods, and successfully LE-specialize word vectors for languages without any external lexical knowledge
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