Learning to Rank
Related Papers
An Empirical Study on Learning to Rank of Tweets
Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. …...
An Empirical Study on Web Mining of Parallel Data
This paper 1 presents an empirical ap- proach to mining parallel corpora. Con- ventional approaches use a readily available collection of comparable, non- parallel corpora to extract parallel sen- tences. …...
Learning to Link Entities with Knowledge Base
This paper address the problem of entity link- ing. Specifically, given an entity mentioned in unstructured texts, the task is to link this entity with an entry stored in the …...
RelationListwise for Query-Focused Multi-Document Summarization
Most existing learning to rank based summarization methods only used content relevance of sentences with respect to queries to rank or estimate sentences, while neglecting sentence relationships. In our work, …...
Efficient Collective Entity Linking with Stacking
Entity disambiguation works by linking am- biguous mentions in text to their correspond- ing real-world entities in knowledge base. Re- cent collective disambiguation methods en- force coherence among contextual decisions …...
Learning to Rank Lexical Substitutions
The problem to replace a word with a syn- onym that fits well in its sentential context is known as the lexical substitution task. In this paper, we tackle this …...
BEER 1.1: ILLC UvA submission to metrics and tuning task
We describe the submissions of ILLC UvA to the metrics and tuning tasks on WMT15. Both submissions are based on the BEER evaluation metric origi- nally presented on WMT14 (Stanojevi´c …...
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents
Most current chatbot engines are designed to reply to user utterances based on exist- ing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach …...
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries
AbstractOrdinal regression which is known with learning to rank has long been used in information retrieval (IR). Learning to rank algorithms, have been tailored in document ranking, information filtering, and …...
News Citation Recommendation with Implicit and Explicit Semantics
In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. …...
QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding
Resorting to community question answer- ing (CQA) websites for finding answers has gained momentum in the past decade with the explosive rate at which social media has been proliferating. With …...
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality
AbstractWhile measuring the readability of texts has been a long-standing research topic, assessing the technicality of terms has only been addressed more recently and mostly for the English language. In …...
Extractive Headline Generation Based on Learning to Rank for Community Question Answering
AbstractUser-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation …...
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank
AbstractDetecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and …...
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description
AbstractIn this paper, we study the problem of identifying the principals and accessories from the fact description with multiple defendants in a criminal case. We treat the fact descriptions as …...
That is a Known Lie: Detecting Previously Fact-Checked Claims
AbstractThe recent proliferation of ”fake news” has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number …...
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
AbstractResponse selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary …...
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search
AbstractAccording to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval. The PRP holds when …...
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference
AbstractThe task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key challenge for …...
OPI at SemEval-2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation
AbstractThe goal of visual word sense disambiguation is to find the image that best matches the provided description of the word’s meaning. It is a challenging problem, requiring approaches that …...