SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment

Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SEMAXIS, a simple yet powerful framework to characterize word semantics using many semantic axes in word- vector spaces beyond sentiment. We demonstrate that SEMAXIS can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SEMAXIS outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.Comment: Accepted in ACL 2018 as a full pape

Situated grounded word semantics

Trabajo presentado a la 16th International Joint Conference on Artificial Intelligence (ijcai) celebrada en Estocolmo (Suecia) del 31 de julio al 6 de agosto de 1999.The paper reports on experiments in which autonomous visually grounded agents bootstrap an ontology and a shared lexicon without prior design nor other forms of human intervention. The agents do so while playing a particular language game called the guessing game. We show that synonymy and polysemy arise as emergent properties in the language but also that there are tendencies to dampen it so as to make the language more coherent and thus more optimal from the viewpoints of communicative success, cognitive complexity, and learnability.This research was conducted at the Sony Computer Science Laboratory.Peer reviewe

Zipf's Law and Avoidance of Excessive Synonymy

Zipf's law states that if words of language are ranked in the order of decreasing frequency in texts, the frequency of a word is inversely proportional to its rank. It is very robust as an experimental observation, but to date it escaped satisfactory theoretical explanation. We suggest that Zipf's law may arise from the evolution of word semantics dominated by expansion of meanings and competition of synonyms.Comment: 47 pages; fixed reference list missing in v.

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape