68,864 research outputs found
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity using Contrastive Learning and Structured Knowledge
Generic sentence embeddings provide a coarse-grained approximation of
semantic textual similarity but ignore specific aspects that make texts
similar. Conversely, aspect-based sentence embeddings provide similarities
between texts based on certain predefined aspects. Thus, similarity predictions
of texts are more targeted to specific requirements and more easily
explainable. In this paper, we present AspectCSE, an approach for aspect-based
contrastive learning of sentence embeddings. Results indicate that AspectCSE
achieves an average improvement of 3.97% on information retrieval tasks across
multiple aspects compared to the previous best results. We also propose using
Wikidata knowledge graph properties to train models of multi-aspect sentence
embeddings in which multiple specific aspects are simultaneously considered
during similarity predictions. We demonstrate that multi-aspect embeddings
outperform single-aspect embeddings on aspect-specific information retrieval
tasks. Finally, we examine the aspect-based sentence embedding space and
demonstrate that embeddings of semantically similar aspect labels are often
close, even without explicit similarity training between different aspect
labels.Comment: Accepted to the 14th International Conference on Recent Advances in
Natural Language Processing (RANLP 2023
The Modulatory Effect of Expectations on Memory Retrieval During Sentence Comprehension
Memory retrieval and probabilistic expectations are recognized factors in sentence comprehension that capture two different critical aspects of processing difficulty: the cost of retrieving and integrating previously processed elements with the new input words and the cost of incorrect predictions about upcoming words or structures in a sentence. Although these two factors have independently received substantial support from the extant literature, how they interact remains poorly understood. The present study investigated memory retrieval and expectation in a single experiment, pitting these factors against each other. Results showed a significant interference effect in both response time to the comprehension questions and reading time at the last (spillover) sentence region. We also found that the interference effect on reading time (but not on comprehension question response time) was canceled when the word at the retrieval site was highly predictable. Overall, our findings are consistent with the hypothesis of a modulatory effect of expectations on memory retrieval and with the idea that expectation-based facilitation results from pre-activation of the target word ahead of time
Role of expectation and working memory constraints in Hindi comprehension: An eyetracking corpus analysis
We used the Potsdam-Allahabad Hindi eye-tracking corpus to investigate the role of word-level and sentence-level factors during sentence comprehension in Hindi. Extending previous work that used this eye-tracking data, we investigate the role of surprisal and retrieval cost metrics during sentence processing. While controlling for word-level predictors (word complexity, syllable length, unigram and bigram frequencies) as well as sentence-level predictors such as integration and storage costs, we find a significant effect of surprisal on first-pass reading times (higher surprisal value leads to increase in FPRT). Effect of retrieval cost was only found for a higher degree of parser parallelism. Interestingly, while surprisal has a significant effect on FPRT, storage cost (another prediction-based metric) does not. A significant effect of storage cost shows up only in total fixation time (TFT), thus indicating that these two measures perhaps capture different aspects of prediction. The study replicates previous findings that both prediction-based and memory-based metrics are required to account for processing patterns during sentence comprehension. The results also show that parser model assumptions are critical in order to draw generalizations about the utility of a metric (e.g. surprisal) across various phenomena in a language
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.Comment: Accepted in IJCAI-1
Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application
We present two novel models of document coherence and their application to
information retrieval (IR). Both models approximate document coherence using
discourse entities, e.g. the subject or object of a sentence. Our first model
views text as a Markov process generating sequences of discourse entities
(entity n-grams); we use the entropy of these entity n-grams to approximate the
rate at which new information appears in text, reasoning that as more new words
appear, the topic increasingly drifts and text coherence decreases. Our second
model extends the work of Guinaudeau & Strube [28] that represents text as a
graph of discourse entities, linked by different relations, such as their
distance or adjacency in text. We use several graph topology metrics to
approximate different aspects of the discourse flow that can indicate
coherence, such as the average clustering or betweenness of discourse entities
in text. Experiments with several instantiations of these models show that: (i)
our models perform on a par with two other well-known models of text coherence
even without any parameter tuning, and (ii) reranking retrieval results
according to their coherence scores gives notable performance gains, confirming
a relation between document coherence and relevance. This work contributes two
novel models of document coherence, the application of which to IR complements
recent work in the integration of document cohesiveness or comprehensibility to
ranking [5, 56]
Explicit diversification of event aspects for temporal summarization
During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness
Aspect-Controlled Neural Argument Generation
We rely on arguments in our daily lives to deliver our opinions and base them
on evidence, making them more convincing in turn. However, finding and
formulating arguments can be challenging. In this work, we train a language
model for argument generation that can be controlled on a fine-grained level to
generate sentence-level arguments for a given topic, stance, and aspect. We
define argument aspect detection as a necessary method to allow this
fine-granular control and crowdsource a dataset with 5,032 arguments annotated
with aspects. Our evaluation shows that our generation model is able to
generate high-quality, aspect-specific arguments. Moreover, these arguments can
be used to improve the performance of stance detection models via data
augmentation and to generate counter-arguments. We publish all datasets and
code to fine-tune the language model
A literature survey of methods for analysis of subjective language
Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area
Concept-based Interactive Query Expansion Support Tool (CIQUEST)
This report describes a three-year project (2000-03) undertaken in the Information Studies
Department at The University of Sheffield and funded by Resource, The Council for
Museums, Archives and Libraries. The overall aim of the research was to provide user
support for query formulation and reformulation in searching large-scale textual resources
including those of the World Wide Web. More specifically the objectives were: to investigate
and evaluate methods for the automatic generation and organisation of concepts derived from
retrieved document sets, based on statistical methods for term weighting; and to conduct
user-based evaluations on the understanding, presentation and retrieval effectiveness of
concept structures in selecting candidate terms for interactive query expansion.
The TREC test collection formed the basis for the seven evaluative experiments conducted in
the course of the project. These formed four distinct phases in the project plan. In the first
phase, a series of experiments was conducted to investigate further techniques for concept
derivation and hierarchical organisation and structure. The second phase was concerned with
user-based validation of the concept structures. Results of phases 1 and 2 informed on the
design of the test system and the user interface was developed in phase 3. The final phase
entailed a user-based summative evaluation of the CiQuest system.
The main findings demonstrate that concept hierarchies can effectively be generated from
sets of retrieved documents and displayed to searchers in a meaningful way. The approach
provides the searcher with an overview of the contents of the retrieved documents, which in
turn facilitates the viewing of documents and selection of the most relevant ones. Concept
hierarchies are a good source of terms for query expansion and can improve precision. The
extraction of descriptive phrases as an alternative source of terms was also effective. With
respect to presentation, cascading menus were easy to browse for selecting terms and for
viewing documents. In conclusion the project dissemination programme and future work are
outlined
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