Introduction General methodology Mention detection ML-based coreference resolution References Coreference Resolution Jordi Turmo TALP Research Center [email protected] 2014 Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Introduction General Goal Types of coreference Identity noun phrase coreference Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References The goal of coreference resolution Determining which mentions in a discourse refer to the same real world entity, property or situation. Example: FC Barcelona president Joan Laporta has warned Chelsea off star strike Lionel Messi. This warning has generated dicouragement in Chelsea. Aware of Chelsea owner Roman Abramovich’s interest in the young Argentine, Laporta said last night: ” I will answer as always, Messi is not for sale and we do not want to let him go.” Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References The goal of coreference resolution Determining which mentions in a discourse refer to the same real-world entity, property or situation. Example: FC Barcelona president Joan Laporta has warned Chelsea off star strike Lionel Messi. This warning has generated dicouragement in Chelsea. Aware of Chelsea owner Roman Abramovich’s interest in the young Argentine, Laporta said last night: ” I will answer as always, Messi is not for sale and we do not want to let him go.” Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References The goal of coreference resolution Determining which mentions in a discourse refer to the same real world entity, property or situation. Example: FC Barcelona president Joan Laporta has warned Chelsea off star strike Lionel Messi. This warning has generated dicouragement in Chelsea. Aware of Chelsea owner Roman Abramovich’s interest in the young Argentine, Laporta said last night: ” I will answer as always, Messi is not for sale and we do not want to let him go.” Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Introduction General Goal Types of coreference Identity noun phrase coreference Jordi Turmo TALP Research Center [email protected] Coreference Resolution I Cataphora (endophora): I We do not want to let [him]1 go. [Messi]1 is not for sale. I Exophora: I Smoking is forbidden [here]1. I [That chair]1 is broken. Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Types of coreference (positional viewpoint) Given two mentions, I Anaphora (endophora): I [Messi]1 is not for sale. We do not want to let [him]1 go. I [Laporta warned Chelsea off Messi]1.[This warning]1 generated discouragement in Chelsea. I [The car]1 hit a tree. [The vehicle]1 was found one day later. Jordi Turmo TALP Research Center [email protected] Coreference Resolution I Exophora: I Smoking is forbidden [here]1. I [That chair]1 is broken. Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Types of coreference (positional viewpoint) Given two mentions, I Anaphora (endophora): I [Messi]1 is not for sale. We do not want to let [him]1 go. I [Laporta warned Chelsea off Messi]1.[This warning]1 generated discouragement in Chelsea. I [The car]1 hit a tree. [The vehicle]1 was found one day later. I Cataphora (endophora): I We do not want to let [him]1 go. [Messi]1 is not for sale. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Types of coreference (positional viewpoint) Given two mentions, I Anaphora (endophora): I [Messi]1 is not for sale. We do not want to let [him]1 go. I [Laporta warned Chelsea off Messi]1.[This warning]1 generated discouragement in Chelsea. I [The car]1 hit a tree. [The vehicle]1 was found one day later. I Cataphora (endophora): I We do not want to let [him]1 go. [Messi]1 is not for sale. I Exophora: I Smoking is forbidden [here]1. I [That chair]1 is broken. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Coreference vs. anaphora: controversy I Bound variable (semanticists): anaphoric mentions in which no particular real world entity is involved. They are not coreferent mentions. I [Every dog]1 has [its]1 day. I Non-identity coreference relation: anaphoric coreferring mentions involving different entities (meronymy/holonymy) I The boy entered [the room]1. The [door]1 closed automatically. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Identity noun phrase coreference I Determining which mentions in a discourse refer to the same real-world entity. I A mention is an expression which refers to an entity. A noun phrase. I An entity or coreference chain is the group of mentions that refer to the same real-word entity I Most commonly investigated kind of coreference relation. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Identity noun phrase coreference I Included examples: I [Messi]1 is not for sale. We do not want to let [him]1 go. I [The car]1 hit a tree. [The vehicle]1 was found one day later. I [Bruce Springsteen]1 will play in Barcelona. [The Boss]1 is well liked in that place. I We do not want to let [him]1 go. [Messi]1 is not for sale. I Excluded examples: I [Every dog]1 has [its]1 day. I The boy entered [the room]1. The [door]1 closed automatically. I Smoking is forbidden [here]1. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology General Goal Mention detection Types of coreference ML-based coreference resolution Identity noun phrase coreference References Identity noun phrase coreference I Included examples: I [Messi]1 is not for sale. We do not want to let [him]1 go. I [The car]1 hit a tree. [The vehicle]1 was found one day later. I [Bruce Springsteen]1 will play in Barcelona. [The Boss]1 is well liked in that place. I We do not want to let [him]1 go. [Messi]1 is not for sale. WE WILL FOCUS ON THEM!! For simplicity, from now, we will refer to identity noun phrase coreference resolution simply as coreference resolution. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Preprocessing: I Mention detection: I Detects the boundaries of the mentions in the input text. I system mentions vs true mentions I m = (m1; m2;:::; mn) ordered as found in the document. Introduction General methodology Mention detection ML-based coreference resolution References General methodology of a coreference solver Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology Mention detection ML-based coreference resolution References General methodology of a coreference solver Preprocessing: I Mention detection: I Detects the boundaries of the mentions in the input text. I system mentions vs true mentions I m = (m1; m2;:::; mn) ordered as found in the document. Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology Mention detection ML-based coreference resolution References General methodology of a coreference solver Coreference resolution: I find the coreference chains. I Heuristic-driven approaches: based on the centering theory of the discourse [Grosz et al., 83, 95]. See details in [Walker et al., 98]. I ML-based approaches: WE WILL FOCUS ON THEM! Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology Mention detection ML-based coreference resolution References Mention detection Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology Mention detection ML-based coreference resolution References Mention detection I Preprocess: POS-tagging, NERC and parsing. I Recursiverly visiting the parse tree, accept the following as mention I Pronouns (filter out pleonastic pronouns, e.g., It is raining) I Proper names I Maximal noun phrase (NP) projections. I Coordinated NPs Jordi Turmo TALP Research Center [email protected] Coreference Resolution Introduction General methodology