2016/10/27 by Vered Shwartz, Shwartz, Vered, Ido Dagan +1
Computer Science · Engineering · Psychology · #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Data mining #Engineering #FOS: Computer and information sciences #Identification (biology) #Information retrieval #Linguistics #Natural Language Processing Techniques #Natural language processing #Path (computing) #Psychology #Relation (database) #Semantic Web #Semantic computing #Semantic integration #Semantic relation #Semantic similarity #Similarity (geometry) #Task (project management) #Text Readability and Simplification #Topic Modeling #Word (group theory) #cs.CL
paper · pdf · doi:10.48550/arxiv.1610.08694
5 pages, accepted to the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V), in COLING 2016
openalex publication_date 2016/10/27 · arxiv created 2016/11/01 · arxiv updated 2016/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations. Combined with a common similarity measure, LexNET performs fairly good on the word relatedness task (subtask 1). The relatively low performance of LexNET and all other systems on subtask 2, however, confirms the difficulty of the semantic relation classification task, and stresses the need to develop additional methods for this task.