A method for assisting with scheduling a meeting, the method performed by a digital assistant service comprising: receiving an electronic message (105; 205; 305) comprising a plurality of sentences (216, 218, 222, 224; 316, 318, 320) composed by a first user (106; 206; 306) to a second user and addressed to a digital assistant associated with the digital assistant service; extracting each sentence from the electronic message (105; 205; 305); applying a word encoder to each word for each sentence utilizing a coarse-grained processing model (212; 312) comprising a hierarchical attention model; determining, based on the applied word encoders, a resulting vector for each sentence utilizing the course-grained processing model (212; 312); identifying , utilizing the coarse-grained processing model (212; 312), a subset of sentences (214; 314) of the plurality of sentences that are relevant to scheduling of the meeting, based on the sentences meeting a minimum threshold value; identifying , utilizing a fine-grained processing model (226; 322), a subset of words in the identified subset of sentences (214; 314) that are potentially relevant to scheduling of the meeting based on relating to at least one meeting parameter; splitting , utilizing the fine-grained processing model (226; 322), the identified subset of words into a first group comprising words from the subset of words that are above a meeting relevance threshold value, and a second group comprising words from the subset of words that are below a meeting relevance threshold value; and causing an automated action associated with scheduling the meeting to be performed, wherein the automated action includes at least one meeting parameter from one of the words of the first group of words. receiving an electronic message (105; 205; 305) comprising a plurality of sentences (216, 218, 222, 224; 316, 318, 320) composed by a first user (106; 206; 306) to a second user and addressed to a digital assistant associated with the digital assistant service; extracting each sentence from the electronic message (105; 205; 305); applying a word encoder to each word for each sentence utilizing a coarse-grained processing model (212; 312) comprising a hierarchical attention model; determining, based on the applied word encoders, a resulting vector for each sentence utilizing the course-grained processing model (212; 312); identifying , utilizing the coarse-grained processing model (212; 312), a subset of sentences (214; 314) of the plurality of sentences that are relevant to scheduling of the meeting, based on the sentences meeting a minimum threshold value; identifying , utilizing a fine-grained processing model (226; 322), a subset of words in the identified subset of sentences (214; 314) that are potentially relevant to scheduling of the meeting based on relating to at least one meeting parameter; splitting , utilizing the fine-grained processing model (226; 322), the identified subset of words into a first group comprising words from the subset of words that are above a meeting relevance threshold value, and a second group comprising words from the subset of words that are below a meeting relevance threshold value; and causing an automated action associated with scheduling the meeting to be performed, wherein the automated action includes at least one meeting parameter from one of the words of the first group of words. <EOS>