This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7
on Semantic Relation Extraction and Classification in Scientific Papers. First
we extract the shortest dependency path (sdp) between two entities, then we
introduce a convolutional neural network (CNN) which takes the shortest
dependency path embeddings as input and performs relation classification with
differing objectives for each subtask of the shared task. This approach
achieved overall F1 scores of 76.7 and 83.2 for relation classification on
clean and noisy data, respectively. Furthermore, for combined relation
extraction and classification on clean data, it obtained F1 scores of 37.4 and
33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared
task.

本文介绍了一种基于最短依赖路径和卷积神经网络的方法，用于在科技论文中进行语义关系的提取和分类。该方法在任务中排名第三，对于干净数据和有噪声数据的关系分类整体 F1 得分分别为 76.7 和 83.2。此外，对于干净数据的综合关系提取和分类，它获得了 37.4 和 33.6 的 F1 得分。

SIRIUS-LTG-UiO 在 SemEval-2018 Task 7 中的应用：短依存路径卷积神经网络用于科学论文中的语义关系提取与分类

SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks  with Shortest Dependency Paths for Semantic Relation Extraction and  Classification in Scientific Papers

Recursive neural models, which use syntactic parse trees to recursively
generate representations bottom-up, are a popular architecture. But there have
not been rigorous evaluations showing for exactly which tasks this syntax-based
method is appropriate. In this paper we benchmark {\bf recursive} neural models
against sequential {\bf recurrent} neural models (simple recurrent and LSTM
models), enforcing apples-to-apples comparison as much as possible. We
investigate 4 tasks: (1) sentiment classification at the sentence level and
phrase level; (2) matching questions to answer-phrases; (3) discourse parsing;
(4) semantic relation extraction (e.g., {\em component-whole} between nouns).
Our goal is to understand better when, and why, recursive models can
outperform simpler models. We find that recursive models help mainly on tasks
(like semantic relation extraction) that require associating headwords across a
long distance, particularly on very long sequences. We then introduce a method
for allowing recurrent models to achieve similar performance: breaking long
sentences into clause-like units at punctuation and processing them separately
before combining. Our results thus help understand the limitations of both
classes of models, and suggest directions for improving recurrent models.

本文比较了基于语法树递归生成表征的递归神经模型与基于序列生成表征的循环神经模型（包括简单循环和 LSTM 模型）在情感分类、问答匹配、话语解析和语义关系提取等 4 个任务上的效果，并提出一种将长句子分解为类从句单元再分别处理的方法以提高循环模型的性能。研究结果表明递归模型在头词之间相隔较远、序列较长的任务上可优于循环模型，同时也揭示了两类模型的局限性以及未来改进方向。