13 research outputs found
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
A CONSTRUCTIVE FINE-GRAINED OPINION REVIEW SERVICE FOR EXTRACTION
A point of view target is certainly an item concerning which clients will convey their opinions, generally as nouns otherwise phrases of nouns. Opinion targets additionally to extraction of opinion word aren't novel tasks within opinion mining. Inside our work we advise a method that is dependent on partially-supervised kind of alignment that will help in identification of opinion relations as the whole process of alignment. Our work focus on recognition of opinion relations among opinion targets additionally to opinion words. Candidates by means of advanced confidence are located as opinion targets. When compared to traditional kinds of not being watched alignment, forecasted model will acquire enhanced precision due to practice of partial supervision. Our representation will captures opinion relations more precisely, designed for extended-span relations when compared to earlier techniques that are on first step toward nearest-neighbour rules
Opinion Holder and Target Extraction for Verb-based Opinion Predicates – The Problem is Not Solved
We offer a critical review of the current state of opinion role extraction involving opinion verbs. We argue that neither the currently available lexical resources nor the manually annotated text corpora are sufficient to appropriately study this task. We introduce a new corpus focusing on opinion roles of opinion verbs from the Subjectivity Lexicon and show potential benefits of this corpus. We also demonstrate that state-of-the-art classifiers perform rather poorly on this new dataset compared to the standard dataset for the task showing that there still remains significant research to be done
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA,
which aims to extract the corresponding opinion words for a given opinion
target in a sentence. Recently, neural network methods have been applied to
this task and achieve promising results. However, the difficulty of annotation
causes the datasets of TOWE to be insufficient, which heavily limits the
performance of neural models. By contrast, abundant review sentiment
classification data are easily available at online review sites. These reviews
contain substantial latent opinions information and semantic patterns. In this
paper, we propose a novel model to transfer these opinions knowledge from
resource-rich review sentiment classification datasets to low-resource task
TOWE. To address the challenges in the transfer process, we design an effective
transformation method to obtain latent opinions, then integrate them into TOWE.
Extensive experimental results show that our model achieves better performance
compared to other state-of-the-art methods and significantly outperforms the
base model without transferring opinions knowledge. Further analysis validates
the effectiveness of our model.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
Existing aspect extraction methods mostly rely on explicit or ground truth
aspect information, or using data mining or machine learning approaches to
extract aspects from implicit user feedback such as user reviews. It however
remains under-explored how the extracted aspects can help generate more
meaningful recommendations to the users. Meanwhile, existing research on
aspect-based recommendations often relies on separate aspect extraction models
or assumes the aspects are given, without accounting for the fact the optimal
set of aspects could be dependent on the recommendation task at hand.
In this work, we propose to combine aspect extraction together with
aspect-based recommendations in an end-to-end manner, achieving the two goals
together in a single framework. For the aspect extraction component, we
leverage the recent advances in large language models and design a new prompt
learning mechanism to generate aspects for the end recommendation task. For the
aspect-based recommendation component, the extracted aspects are concatenated
with the usual user and item features used by the recommendation model. The
recommendation task mediates the learning of the user embeddings and item
embeddings, which are used as soft prompts to generate aspects. Therefore, the
extracted aspects are personalized and contextualized by the recommendation
task. We showcase the effectiveness of our proposed method through extensive
experiments on three industrial datasets, where our proposed framework
significantly outperforms state-of-the-art baselines in both the personalized
aspect extraction and aspect-based recommendation tasks. In particular, we
demonstrate that it is necessary and beneficial to combine the learning of
aspect extraction and aspect-based recommendation together. We also conduct
extensive ablation studies to understand the contribution of each design
component in our framework