Software reuse is the use of software resources from all stages of the software development process in new applications. Rather the opportunistically reuse (e.g., by cutting and pasting code snippets from existing programs into new programs), we consider here the systematic reuse. This paper introduces a novel framework that helps identify automatically the segments of software that can be reus...

We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed mode...

Recognizing Textual Entailment is a task that recognizes pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Textual Entailment is useful in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine transla...

我們所參與公開評測 NTCIR10 RITE-2[5]將文字蘊涵的研究分成兩種層面,首先是分兩 類(Binary Class, BC) ,任務的目標是單純判別 T1 與 T2 之間是否具有蘊涵關係。但句 子之間蘊涵關係並不能單純以有或沒有這麼簡單就區分開,NTCIR RITE 另外定義多類 (Multi Class, MC)這項任務,將句子之間的蘊涵分類為正向、雙向、矛盾、與獨立四種 關係。假設這個句子對具有蘊涵關係,但有可能兩個句子所包涵的資訊數量不同,造成 我們只能從其中一個句子推論出另一個句子的完整的意思,這樣的情況我們稱為兩個句 子間的蘊涵關係為正向蘊涵。反之兩個句子可以互相推論出另一個句子的含意,這樣的 情況我們就稱為雙向蘊涵關係。假設句子對之間沒有蘊涵關係,我們可以很合理認為兩 個句子所表達的意思不相同,但這並不完全正確的想法。可能兩個句子所包涵的資訊大 致相同只是少部份...

This paper describes the Recognizing Textual Entailment (RTE) system that our teams developed for TAC 2011. Our system combines the entailment score calculated by lexicallevel matching with the machine-learningbased filtering mechanism using various features obtained from lexical-level, chunk-level and predicate argument structure-level information. In the filtering mechanism, we try to discard...

We challenge the NLP community to participate in a large-scale, distributed effort to design and build resources for developing and evaluating solutions to new and existing NLP tasks in the context of Recognizing Textual Entailment. We argue that the single global label with which RTE examples are annotated is insufficient to effectively evaluate RTE system performance; to promote research on s...

Semantic analysis and annotation of textual information with appropriate semantic entities is an essential task to enable content based search on the annotated data. For video resources textual information is rare at first sight. But in recent years the development of technologies for automatic extraction of textual information from audio visual content has advanced. Additionally, video portals...

This paper presents our submitted experiments in the Concept annotation and Concept Retrieval tasks using Flickr photos at ImageCLEF 2012. This edition we applied new strategies for both the textual and the visual subsystems included in our multimodal retrieval system. The visual subsystem has focus on extending the low-level features vector with concept features. These concept features have be...