Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation
نویسندگان
چکیده
منابع مشابه
Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction
In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is to connect two words (w1 and w2) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w1 + r ≈ w2 in the low-dimensional space, where the multihop dependen...
متن کاملDependency Tree Kernels for Relation Extraction
We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find tha...
متن کاملImproving Term Extraction with Terminological Resources
Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. Facing the difficulty or impossibility to customize existing tools, we developed a tunable term extractor. It exploits linguistic-based rules in combination with the reuse of existing terminologies, i.e. exogenous disambiguation. Experiments reported...
متن کاملDLIREC: Aspect Term Extraction and Term Polarity Classification System
This paper describes our system used in the Aspect Based Sentiment Analysis Task 4 at the SemEval-2014. Our system consists of two components to address two of the subtasks respectively: a Conditional Random Field (CRF) based classifier for Aspect Term Extraction (ATE) and a linear classifier for Aspect Term Polarity Classification (ATP). For the ATE subtask, we implement a variety of lexicon, ...
متن کاملLTAG Dependency Parsing with Bidirectional Incremental Construction
In this paper, we first introduce a new architecture for parsing, bidirectional incremental parsing. We propose a novel algorithm for incremental construction, which can be applied to many structure learning problems in NLP. We apply this algorithm to LTAG dependency parsing, and achieve significant improvement on accuracy over the previous best result on the same data set.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE/ACM Transactions on Audio, Speech, and Language Processing
سال: 2019
ISSN: 2329-9290,2329-9304
DOI: 10.1109/taslp.2019.2913094