A　reading　comprehension　(RC)　system　aims　to　understand　a　single　document　(i.e.　story　or　passage)　in　order　to　be　able　to　automatically　answer　questions　about　it.　RC　resembles　the　ad　hoc　question　answering　(QA)　task　that　aims　to　extract　an　answer　from　a　collection　of　documents　when　posed　with　a　question.　However,　since　RC　focuses　only　on　a　single　document,　the　system　needs　to　draw　upon　external　knowledge　sources　to　achieve　deep　analysis　of　passage　sentences　for　answer　sentence　extraction.　Proposed　in　this　paper　is　an　approach　towards　RC　that　attempts　to　utilize　external　knowledge　to　improve　performance,　including　(i)　Automatic　acquisition　of　Web-based　answer　patterns　and　its　application　in　answer　sentence　matching;　(ii)　Linguistic　feature　matching;　(iii)　Lexical　semantic　relation　inference,　and　(iv)　Context　assistance.　This　approach　gives　improved　RC　performances　for　both　the　Remedia　and　ChungHwa　corpora,　attaining　HumSent　accuracies　of　45%　and　69%　respectively.　In　particular,　performance　analysis　based　on　Remedia　shows　that　relative　performances　of　24.1%　is　due　to　the　application　of　Web-derived　answer　patterns　and　a　further　11.1%　is　due　to　linguistic　feature　matching.　Pairwise　t-tests　are　also　conducted　and　the　result　shows　that　the　performance　improvements　due　to　Web-derived　answer　patterns,　linguistic　feature　matching　and　lexical　semantic　relation　inference　technique　are　statistically　significant.