49,620 research outputs found
Reversive constructions in Latin: the case of re- (and dis-)
This paper proposes a cognitive account on re- and dis- verbs based on the scrutiny of the Plautine corpus and Cato’s De agricultura. Re- and dis- exhibit significant differences as to the manner in which they come to a reversive function, and these differences can be traced back to the basic conceptual import of the two prefixes: while dis- is schematically connected with the idea of separation into two parts, re- basically refers to a rearward/reditive trajectory, connecting a point that has already been reached to the starting point. On the basis of this description, I analyze the semantic network of re- and dis- and the role of their conceptual structure in the spread from spatial to reversive values
Learning Sentence-internal Temporal Relations
In this paper we propose a data intensive approach for inferring
sentence-internal temporal relations. Temporal inference is relevant for
practical NLP applications which either extract or synthesize temporal
information (e.g., summarisation, question answering). Our method bypasses the
need for manual coding by exploiting the presence of markers like after", which
overtly signal a temporal relation. We first show that models trained on main
and subordinate clauses connected with a temporal marker achieve good
performance on a pseudo-disambiguation task simulating temporal inference
(during testing the temporal marker is treated as unseen and the models must
select the right marker from a set of possible candidates). Secondly, we assess
whether the proposed approach holds promise for the semi-automatic creation of
temporal annotations. Specifically, we use a model trained on noisy and
approximate data (i.e., main and subordinate clauses) to predict
intra-sentential relations present in TimeBank, a corpus annotated rich
temporal information. Our experiments compare and contrast several
probabilistic models differing in their feature space, linguistic assumptions
and data requirements. We evaluate performance against gold standard corpora
and also against human subjects
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task.Comment: 10 pages, 5 figures, accepted by CVPR 201
SensEmbed: Learning sense embeddings for word and relational similarity
Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including semantic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In addition, their learning process usually relies on massive corpora only, preventing them from taking advantage of structured knowledge. We address both issues by proposing a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement. We evaluate our approach on word similarity and relational similarity frameworks, reporting state-of-the-art performance on multiple datasets
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
In many applications, it is important to characterize the way in which two
concepts are semantically related. Knowledge graphs such as ConceptNet provide
a rich source of information for such characterizations by encoding relations
between concepts as edges in a graph. When two concepts are not directly
connected by an edge, their relationship can still be described in terms of the
paths that connect them. Unfortunately, many of these paths are uninformative
and noisy, which means that the success of applications that use such path
features crucially relies on their ability to select high-quality paths. In
existing applications, this path selection process is based on relatively
simple heuristics. In this paper we instead propose to learn to predict path
quality from crowdsourced human assessments. Since we are interested in a
generic task-independent notion of quality, we simply ask human participants to
rank paths according to their subjective assessment of the paths' naturalness,
without attempting to define naturalness or steering the participants towards
particular indicators of quality. We show that a neural network model trained
on these assessments is able to predict human judgments on unseen paths with
near optimal performance. Most notably, we find that the resulting path
selection method is substantially better than the current heuristic approaches
at identifying meaningful paths.Comment: In Proceedings of the Web Conference (WWW) 201
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